00425nas a2200157   4500000000100000000000100001000000100002008004100003100001400044700002400058700002100082700001600103700001700119245012400136490000700260       2002                            d1 aA. Badrah1 aTadeusz Czachórski1 aJoanna Domańska1 aJ. Fourneau1 aF. Quessette00aPerformance evaluation of multistage interconnection networks with blocking - discrete and continuous time Markov model0 v1400383nas a2200133   4500000000100000000000100001008004100002260000800043100001300051700002400064700001600088700002200104245012300126       2007                            d  bACM1 aA. Busic1 aTadeusz Czachórski1 aJ. Fourneau1 aKrzysztof Grochla00aLevel Crossing Ordering of Markov Chains: Proving Convergence and Bounding End to End Delays in an All Optical Network01111nas a2200157   4500000000100000000000100001008004100002260003600043100001900079700002400098700002200122700002400144245007000168300001400238520070100252       2010                            d  bIITiS PANaGliwice, Bałtycka 51 aAdam Józefiok1 aTadeusz Czachórski1 aKrzysztof Grochla1 aTadeusz Czachórski00aPerformance evaluation of multiuser interactive networking system  a223–2313 aThe article presents a work in progress aiming at performance evaluation of a large database system at an assurance company. The system includes a server with a database and a local area network with a number of terminals where the company employees run applications that introduce documents or retrieve them from the database. We discuss a model of clients activities. Measurements were collected inside the working system: the phases at each users application were identified and their duration was measured. The collected data are used to construct a synthetic model of applications activities which is then applied to predict the system bahaviour in case of the growth of the number of users.00435nas a2200145   4500000000100000000000100001008004100002260005300043100001300096700001300109700002400122700002000146700002000166245010300186       2005                            d  bWydawnictwa Komunikacji i ŁącznościaWarszawa1 aP. Bazan1 aJ. Krzak1 aTadeusz Czachórski1 aAndrzej Grzywak1 aPiotr Pikiewicz00aNowe Technologie w Komputerowych Systemach Zarządzania. Internet w Społeczeństwie Informacyjnym00343nas a2200121   4500000000100000000000100001008004100002260004600043100001600089700002400105700002200129245007000151       2007                            d  c6bWydawnictwo Komunikacji i Łączności1 aA. Brachman1 aTadeusz Czachórski1 aKrzysztof Grochla00aAnaliza wydajności protokołu TCP-NCR w sieciach bezprzewodowych00437nas a2200169   4500000000100000000000100001008004100002260001800043100001400061700001400075700002400089700002100113700002000134700002500154700002400179245006400203       2004                            d  bWNTaWarszawa1 aB. Bylina1 aJ. Bylina1 aTadeusz Czachórski1 aJoanna Domańska1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWspółczesne problemy sieci komputerowych Nowe technologie00458nas a2200157   4500000000100000000000100001000000100002008004100003100001400044700001400058700002100072700002400093245016200117300001400279490000700293       2004                            d1 aB. Bylina1 aJ. Bylina1 aJoanna Domańska1 aTadeusz Czachórski00aThe use of vectorized and parallelized solutions to Markovian models in performance evaluation of communication networks: a case of leaky buckets with tokens  a153–1700 v1600306nas a2200109   4500000000100000000000100001008004100002260006200043100002400105700002000129245004700149       2003                            d  bWyższa Szkoła Biznesu i OrganizacjiaDąbrowa Górnicza1 aTadeusz Czachórski1 aAndrzej Grzywak00aInternet w społeczeństwie informatycznym00463nas a2200145   4500000000100000000000100001008004100002260008600043100002400129700001700153700002000170700001500190245010200205300001000307       2013                            d  c8bPracownia Komputerowa Jacka Skalmierskiegoa44-100 Gliwice, ul. Pszczyńska 441 aTadeusz Czachórski1 aV. Mityushev1 aŁ.A. Stępień1 aA. Budziak00aQueueing models: transient state analysis applied to performance evaluation of computer networks.  a5–601996nas a2200133   4500000000100000000000100001008004100002260005000043100002400093700001700117700001700134245005500151520165600206       2015                            d  bSpringer International Publishing Switzerland1 aTadeusz Czachórski1 aV. Mityushev1 aM. Ruzhansky00aAnalytic Methods in Interdisciplinary Applications3 aQueueing theory is a useful tool in design of computer networks and their performance evaluation. The literature concerning this subject is abundant. However, it is in general limited to the analysis of steady states. It means that flows of customers considered in models are constant and obtained solutions do not depend on time. It is in glaring contrast with the flows observed in real networks where the perpetual changes of traffic intensities are due to the nature of users, sending variable quantities of data, cf. multimedia traffic, and also due to the performance of traffic control algorithms which are trying to avoid congestion in networks, e.g. the algorithm of congestion window used in TCP protocol which is adapting the rate of the sent traffic to the observed losses or transmission delays. We discuss here the means used to analyse transient states in queueing models. In computer applications a mathematical model is useful only when it furnishes quantitative results. Therefore practical ussues related to numerical side of models are of importance and are here discussed. We present three approaches - Markov models solved numerically, fluid flow approximation and diffusion approximation. A particular importance is given to the latter as the author has here over 20year experience in development and application of this method. He is also convinced of the qualities of this approach - its flexibility to treat various variants of queueing models. Traffic intensity observed in computer networks have a complex stochastic nature that influences the network performances. We discuss also this side of implemented queueing models.00461nas a2200109   4500000000100000000000100001008004100002260017700043100002400220245009500244300001200339       2013                            d  c10bV.A.Trapeznikov Institute of Control Sciences Russian Academy of Sciences; Institute of Information and Communication technologies Bulgarian Academy of SciencesaMoscow1 aTadeusz Czachórski00aModelling transient states in queueing models of computer networks: a few practical issues  a27–2800211nas a2200085   4500000000100000000000100001008004100002100002400043245005800067       2013                            d1 aTadeusz Czachórski00aModelling and controlling dynamics of Internet flows.00239nas a2200085   4500000000100000000000100001008004100002100002400043245008600067       2013                            d1 aTadeusz Czachórski00aTransient queueing models applied to performance evaluation of computer networks.00289nas a2200109   4500000000100000000000100001008004100002100002400043245009200067300001300159490000700172       2003                            d1 aTadeusz Czachórski00aAnalityczne modele kolejkowe w ocenie efektywności pracy systemów i sieci kolejkowych  a90–1110 v1600343nas a2200133   4500000000100000000000100001008004100002100002400043700002100067700002400088700001600112245006700128300001400195       2004                            d1 aTadeusz Czachórski1 aJoanna Domańska1 aTadeusz Czachórski1 aN. Pekergin00aMarkovian models for long-range dependent traffic, new results  a213–22800333nas a2200133   4500000000100000000000100001000000100002008004100003100002400044700002100068245008900089300001400178490000700192       2002                            d1 aTadeusz Czachórski1 aJoanna Domańska00aWpływ samopodobnej natury ruchu na długości kolejek w przełącznikach sieciowych  a201–2120 v2300298nas a2200133   4500000000100000000000100001000000100002008004100003100002400044700002100068245005400089300001400143490000700157       2001                            d1 aTadeusz Czachórski1 aJoanna Domańska00aMarkovian models for long-range dependent traffic  a297–3080 v1300449nas a2200145   4500000000100000000000100001008004100002260005300043100002400096700002100120700001900141700002000160700002000180245010300200       2005                            d  bWydawnictwa Komunikacji i ŁącznościaWarszawa1 aTadeusz Czachórski1 aJoanna Domańska1 aAdam Domański1 aAndrzej Grzywak1 aPiotr Pikiewicz00aNowe Technologie w Komputerowych Systemach Zarządzania. Internet w Społeczeństwie Informacyjnym00441nas a2200157   4500000000100000000000100001008004100002260005300043100002400096700002100120700001900141700002000160700002500180700002400205245005400229       2005                            d  bWydawnictwa Komunikacji i ŁącznościaWarszawa1 aTadeusz Czachórski1 aJoanna Domańska1 aAdam Domański1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWysokowydajne sieci komputerowe. Nowe Technologie00359nas a2200121   4500000000100000000000100001008004100002260002600043100002400069700002100093700002200114245010100136       2006                            d  bPolitecnico de Torino1 aTadeusz Czachórski1 aJoanna Domańska1 aKrzysztof Grochla00aDynamics of TCP/IP flows in presence of various AQM algorithms, a fluid flow approximation model00338nas a2200121   4500000000100000000000100001008004100002260004200043100002400085700002100109700002200130245006400152       2006                            d  cVIbTechnical University of Catalonia1 aTadeusz Czachórski1 aJoanna Domańska1 aKrzysztof Grochla00aTCP/IP dynamics in wireless environment, a fluid-flow model00636nas a2200133   4500000000100000000000100001008004100002100002400043700002100067700002100088245006900109490000700178520031700185       2001                            d1 aTadeusz Czachórski1 aJoanna Domańska1 aArkadiusz Sochan00aSamopodobny charakter natężenia ruchu w sieciach komputerowych0 v223 aArtykuł przedstawia analizę natężenia ruchu pakietów w sieci Ethernet oraz ruchu ramek MPEG-2 w sieci ATM pod kątem występowania cechy zwanej samopodobieństwem. Zamieszczono krótką charakterystykę procesów samopodobnych oraz wnioski wypływające z samopodobnej natury ruchu w sieciach komputerowych.00324nas a2200133   4500000000100000000000100001008004100002260001800043100002400061700001900085700002100104700002000125245004500145       2004                            d  bWNTaWarszawa1 aTadeusz Czachórski1 aAdam Domański1 aJoanna Domańska1 aAndrzej Grzywak00aInternet w spoleczenstwie informatycznym00416nas a2200157   4500000000100000000000100001008004100002260001800043100002400061700001900085700002100104700002000125700002500145700002400170245006400194       2004                            d  bWNTaWarszawa1 aTadeusz Czachórski1 aAdam Domański1 aJoanna Domańska1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWspółczesne problemy sieci komputerowych Nowe technologie00314nas a2200121   4500000000100000000000100001008004100002100002400043700001600067700002000083700001700103245007200120       2002                            d1 aTadeusz Czachórski1 aJ. Fourneau1 aSławomir Nowak1 aF. Quessette00aPerformance of a mixed deflection and convergence routing algorithm00327nas a2200121   4500000000100000000000100001008004100002100002400043700002200067700001900089700001600108245008100124       2011                            d1 aTadeusz Czachórski1 aKrzysztof Grochla1 aAdam Józefiok1 aTomasz Nycz00aSimulation, Markov Chain and Diffusion Approximation Models – a Case Study00954nas a2200181   4500000000100000000000100001000000100002008004100003100002400044700002200068700001900090700001600109245009500125300001100220490000700231520052000238022001400758       2011                            d1 aTadeusz Czachórski1 aKrzysztof Grochla1 aAdam Józefiok1 aTomasz Nycz00aPorównanie metod analizy efektywności na przykładzie serwera aplikacji w sieci lokalnej  a7–200 v323 aW artykule przedstawiono model kolejkowy służący do oceny funkcjonowania dużego systemu sieciowego, w skład którego wchodzi rozbudowany system bazodanowy. Opisywany system jest systemem rzeczywistym. Zebrane wyniki pracy systemu posłużyły do budowy modelu działania aplikacji interakcyjnych, na podstawie łańcuchów Markova, aproksymacji dyfuzyjnej i symulacji zdarzeń dyskretnych. Porównanie ich z rzeczywistymi wynikami umożliwiło sprawdzenie przdatności użytych metod w rzeczywistych warunkach.  a1731-226401415nas a2200205   4500000000100000000000100001008004100002260001300043100002400056700002200080700001900102700001600121700002000137700001700157700001700174700002200191245010700213300001400320520087500334       2012                            d  bSpringer1 aTadeusz Czachórski1 aKrzysztof Grochla1 aAdam Józefiok1 aTomasz Nycz1 aFerhan Pekergin1 aErol Gelenbe1 aRicardo Lent1 aGeorgia Sakellari00aPerformance Evaluation of a Multiuser Interactive Networking System: A Comparison of Modelling Methods  a215–2223 aThis article presents a queueing model for performance evaluation of a large database system at an assurance company. The system includes a server with a database and a local area network with a number of terminals where the company employees run applications that introduce documents or retrieve them from the database. We discuss a model of clients activities. Measurements were collected inside the working system: the phases at each users application performance were identified and their duration was measured. The collected data are used to construct a synthetic model of applications activities which is then applied to predict the system behaviour in case of the growth of the number of users. We apply simulation, Markov and diffusion models-their comparison, based on real data, may better verify the utility of particular methods than usual academic examples.01580nas a2200169   4500000000100000000000100001008004100002260002100043100002400064700002200088700002000110700002000130700002100150245005200171300001400223520117300237       2007                            d  c5bSpringeVerlag1 aTadeusz Czachórski1 aKrzysztof Grochla1 aFerhan Pekergin1 aJ. Garcia-Vidal1 al. Cerda-Alabern00aStability and Dynamics of TCP-NCR(DCR) Protocol  a241–2543 aThe fluid-flow approximation models investigate with much success the dynamics and stability of TCP/RED connections. Their main assumption is that the fluctuations of variables characterizing the behaviour of the connections are relatively small, that enables the linearization of model and the use of traditional control analysis tools to obtain such measures as Bode gain, phase margins, tracking error or delay margin. In this article, preserving linear fluid-flow model, we propose its extension to the case when a network is composed of wired and wireless part. We consider a variant of TCP algorithm (TCP-DCR or its new version TCP-NCR) and fluid-flow differential equations representing the size of congestion window, mean queue at the bottleneck router and loss probability at a RED queue are supplemented with terms representing constant loss probability due to transmission in wireless part and probability that a fraction of these errors is recovered by a link level mechanism. The decrease of congestion window due to TCP mechanism is delayed to allow the link protocol to deal with the errors. The model considers the presence of uncontrollable UDP flows.00353nas a2200109   4500000000100000000000100001008004100002100002400043700002200067700002000089245013400109       2008                            d1 aTadeusz Czachórski1 aKrzysztof Grochla1 aFerhan Pekergin00aUn modele d’approximation de diffusion pour la distribution du temps d’acheminement des paquets dans les réseaux de senseurs00311nas a2200109   4500000000100000000000100001008004100002100002400043700002200067700002000089245009200109       2008                            d1 aTadeusz Czachórski1 aKrzysztof Grochla1 aFerhan Pekergin00aEstimation of packet transfer time at sensor networks, diffusion approximation approach00312nas a2200121   4500000000100000000000100001008004100002260000600043100002400049700002200073700002000095245007500115       2008                            d  c21 aTadeusz Czachórski1 aKrzysztof Grochla1 aFerhan Pekergin00aTCP performance for a wireless traffic, a diffusion approxiation model00321nas a2200121   4500000000100000000000100001008004100002260001700043100002400060700002200084700002000106245007300126       2007                            d  c9bIITiS PAN1 aTadeusz Czachórski1 aKrzysztof Grochla1 aFerhan Pekergin00aFluid-flow Approximation Model of TCP-NCR in wired-wireless networks00748nas a2200157   4500000000100000000000100001000000100002008004100003100002400044700002200068700002000090245003400110300001400144490000700158520042500165       2006                            d1 aTadeusz Czachórski1 aKrzysztof Grochla1 aFerhan Pekergin00aA model of TCP flows dynamics  a279–2920 v183 aThe fluid flow approximation models investigate with much success the dynamics and stability of TCP/RED connections. Their main assumption is that the fluctuations of variables characterizing the behaviour of the connections are relatively small, that enables the linearization of model and the use of traditional control analysis tools to obtain such measures as Bode gain, phase margins, tracking error or delay margin00354nas a2200121   4500000000100000000000100001008004100002260000600043100002400049700002200073700002000095245011700115       2006                            d  c91 aTadeusz Czachórski1 aKrzysztof Grochla1 aFerhan Pekergin00aSome contributions to the modelling the dynamics of (TCP,UDP)/RED flows over reliable and unreliable connections01771nas a2200169   4500000000100000000000100001008004100002260011200043100002400155700002200179700001200201700002000213700002400233245007800257300001400335520125200349       2010                            d  c1bInstitute of Theoretical and Applied Informatics of the Polish Academy of SciencesaGliwice, Bałtycka 51 aTadeusz Czachórski1 aKrzysztof Grochla1 aT. Nycz1 aFerhan Pekergin1 aTadeusz Czachórski00aModeling the IEEE 802.11 Networks MAC Layer Using Diffusion Approximation  a429–4423 aAbstract: The article presents an analytical model of wireless networks using the IEEE 802.11 protocol to access the transport medium. The model allows to determine such key factors of the quality of service as transmission delays and losses. The model is based on diffusion approximation approach which was proposed three decades ago to model wired networks. We show that it can be adapted to take into consideration the input streams with general interarrival time distributions and servers with general service time distributions. The diffusion approximation has been chosen because of fairly general assumptions of models based on it, hard to be represented in Markov models. A queueing network model can have an arbitrary topology, the intensity of transmitted flows can be represented by non-Poisson (even self-similar) streams, the service times at nodes can be defined by general distributions. These assumptions are important: because of the CSMA/CA algorithm, the overall times needed to sent a packet are far from being exponentially distributed and therefore the flows between nodes are non-Poisson. Diffusion approximation allows us also to analyse the of transient behaviour of a network when traffic intensity is changing with time.00918nas a2200181   4500000000100000000000100001008004100002260001800043100002400061700002200085700001200107700002000119700002400139700001800163245003700181300001400218520050400232       2012                            d  c12bIITiS PAN1 aTadeusz Czachórski1 aKrzysztof Grochla1 aT. Nycz1 aFerhan Pekergin1 aTadeusz Czachórski1 aMateusz Nowak00aDuration of boot-up storm period  a145–1503 aThe simultaneous boot of multiple virtual machines or networking devices imposes heavy load on the networking infrastructure. In large network of virtual machines the coordination of the reboots and the device registration is required. In the paper we present a simple analytical model to compute the distribution of boot-up time. The model is verified by comparison to a simulation model, and may be used to check the influence of several parameters on the overall boot-up time during a boot storm.01701nas a2200181   4500000000100000000000100001000000100002008004100003100002400044700002200068700001200090700002000102245012000122300001400242490000700256520124200263022001401505       2010                            d1 aTadeusz Czachórski1 aKrzysztof Grochla1 aT. Nycz1 aFerhan Pekergin00aA diffusion approximation model for wireless networks based on IEEE 802.11 standard, Computer Communication Journal  aS86–S920 v333 aThe article presents an analytical model of wireless networks using the IEEE 802.11 protocol to access the transport medium. The model allows to determine such key factors of the quality of service as transmission delays and losses. The model is based on diffusion approximation approach which was proposed three decades ago to model wired networks. We show that it can be adapted to take into consideration the input streams with general interarrival time distributions and servers with general service time distributions. The diffusion approximation has been chosen because of fairly general assumptions of models based on it, hard to be represented in Markov models. A queueing network model can have an arbitrary topology, the intensity of transmitted flows can be represented by non-Poisson (even self-similar) streams, the service times at nodes can be defined by general distributions. These assumptions are important: because of the CSMA/CA algorithm, the overall times needed to sent a packet are far from being exponentially distributed and therefore the flows between nodes are non-Poisson. Diffusion approximation allows us also to analyse the of transient behaviour of a network when traffic intensity is changing with time.  a0140-366400418nas a2200157   4500000000100000000000100001008004100002260004000043100002400083700002400107700001800131700001600149700001400165700001400179245006700193       2002                            d  bIdea Group PublishingaHershey, USA1 aTadeusz Czachórski1 aStanisław Jędruś1 aM. Zakrzewicz1 aJ. Gozdecki1 aP. Pacyna1 aS. Rahman00aMultimedia Networking: Technology, Management and Applications00350nas a2200157   4500000000100000000000100001000000100002008004100003100002400044700001600068700002000084245005500104300001200159490000700171022001400178       2002                            d1 aTadeusz Czachórski1 aJ. Fourneau1 aSławomir Nowak00aSymulacyjne Badanie routingu w sieciach optycznych  a45–580 v2A  a0208-728601084nas a2200157   4500000000100000000000100001008004100002260005000043100002400093700001200117700001200129700001800141245009500159300001200254520066000266       2014                            d  bSpringer International Publishing Switzerland1 aTadeusz Czachórski1 aM. Nycz1 aT. Nycz1 aV. Vishnevsky00aModelling Transient States in Queueing Models of Computer Networks: A Few Practical Issues  a58–723 aThe article summarises author`s experience in two problems related to the use of queueing models in performance evaluation of computer networks: modelling transient states of queues and computations for queueing network models having large number of nodes. Both issues are not well represented in classical queuing theory, yet important to applications, because the observed traffic is time dependant and network topologies that should be considered become larger and larger. The article discusses two approaches: diffusion approximation and fluid-flow approximation that can cope with much larger models that are attainable with the use of Markov chains.00240nas a2200097   4500000000100000000000100001008004100002100002400043700001600067245005900083       2002                            d1 aTadeusz Czachórski1 aPiotr Pecka00aInternet and real-time performance: a web server model00323nas a2200109   4500000000100000000000100001008004100002100002400043700002000067700001900087245010700106       2004                            d1 aTadeusz Czachórski1 aFerhan Pekergin1 aD.D. Kouvatsos00aDiffusion Approximation as a modelling tool in congestion control and performance evaluation, tutorial00409nas a2200145   4500000000100000000000100001008004100002260005300043100002400096700002000120700002000140700002500160700002400185245005400209       2005                            d  bWydawnictwa Komunikacji i ŁącznościaWarszawa1 aTadeusz Czachórski1 aFerhan Pekergin1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWysokowydajne sieci komputerowe, Nowe Technologie01253nas a2200145   4500000000100000000000100001008004100002100002400043700002000067245017500087300001400262490000900276520080800285022001401093       2011                            d1 aTadeusz Czachórski1 aFerhan Pekergin00aDiffusion Approximation as a Modelling Tool, chapter in: Network Performance Engineering, A Handbook on Convergent Multi-Service Networks and Next Generation Internet, St  a447–4760 v52333 aDiffusion theory is already a vast domain of knowledge. This tutorial lecture does not cover all results: it presents in a coherent way an approach we have adopted and used in analysis of a series of models concerning evoluation of some traffic control mechanisms in computer, especially ATM, networks. Diffusion approximation is presented from engineer`s point of view, stressing its utility and commenting numerical problems of its implementation. Diffusion approximation is a method to model the behavior of a single queueing station or a network of stations. It allows one to include in the model general sevice times, general (also correlated)input streams and to investigate transient states, which, in presence of bursty streams (e.g. of multimedia transfers) in modern networks, are of interest.  a0302-974300290nas a2200121   4500000000100000000000100001000000100002008004100003100002400044700002000068245006600088300001400154       2005                            d1 aTadeusz Czachórski1 aFerhan Pekergin00aA diffusion approximation model of an electronic-optical node  a187–19900326nas a2200133   4500000000100000000000100001000000100002008004100003100002400044700002000068245008400088300001300172490000700185       2005                            d1 aTadeusz Czachórski1 aFerhan Pekergin00aA novel diffusion process with jumps to study an electronic-optical edge router  a87–1000 v1700272nas a2200097   4500000000100000000000100001008004100002100002400043700002000067245008700087       2003                            d1 aTadeusz Czachórski1 aFerhan Pekergin00aDiffusion approximation: modelling transient flows of multiclass queueing networks00236nas a2200097   4500000000100000000000100001008004100002100002400043700002000067245005100087       2002                            d1 aTadeusz Czachórski1 aFerhan Pekergin00aA diffusion approximation model of Web servers00259nas a2200097   4500000000100000000000100001008004100002100002400043700002000067245007400087       2001                            d1 aTadeusz Czachórski1 aFerhan Pekergin00aModelling New Congestion Control Mechanisms TCP/IP Internet Protocols00287nas a2200109   4500000000100000000000100001008004100002100002400043700002000067700001300087245007700100       2005                            d1 aTadeusz Czachórski1 aFerhan Pekergin1 aB. Szady00aForming optical packets, a diffusion approximation and simulation models00271nas a2200109   4500000000100000000000100001008004100002100002400043700002000067700001300087245006100100       2005                            d1 aTadeusz Czachórski1 aFerhan Pekergin1 aB. Szady00aForming optical packets, a diffusion approximation model00975nas a2200169   4500000000100000000000100001008004100002260005000043100002400093700001200117700001200129700002000141700002400161245008900185300001400274520051700288       2014                            d  bSpringer International Publishing Switzerland1 aTadeusz Czachórski1 aT. Nycz1 aM. Nycz1 aFerhan Pekergin1 aTadeusz Czachórski00aTraffic Engineering: Erlang and Engset Models Revisited with Diffusion Approximation  a249–2563 aClassical Erlang and Engset formulae determining the availability of channels, loss probability, and characteristics of overflow traffic are still use in telecommunications. Moreover, they are also interesting for traffic management in mobile networks and in Internet. They are based on the assumption of Poisson flows and exponentially distributed time of connections. By means of diffusion approximation queuing models, we extend these results to the case of general distributions and transient state analysis.00368nas a2200145   4500000000100000000000100001008004100002100001300043700001600056700002400072700001600096700001700112700001200129245008100141       2002                            d1 aD. Barth1 aP. Berthome1 aTadeusz Czachórski1 aJ. Fourneau1 aCh. Laforest1 aS. Vial00aA Mixed Deflection and Convergence Routing Algorithm: Design and Performance00325nas a2200133   4500000000100000000000100001000000100002008004100003100002100044700002400065245008100089300001400170490000700184       2003                            d1 aJoanna Domańska1 aTadeusz Czachórski00aWpływ samopodobnej natury ruchu na zachowanie mechanizmu cieknącego wiadra  a199–2120 v2400309nas a2200133   4500000000100000000000100001000000100002008004100003100002100044700002400065245006700089300001200156490000700168       2003                            d1 aJoanna Domańska1 aTadeusz Czachórski00aModel formowania pakietów w węźle brzegowym sieci optycznej  a51–610 v2400734nas a2200157   4500000000100000000000100001008004100002260003200043100002100075700001900096700002400115700001600139245005700155300001400212520035000226       2011                            d  bSpringeraBerlin Heidelberg1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aS. Balandin00aInternet Traffic Source Vased on Hidden Markov Model  a395–4043 aThis article shows how to use Hidden Markov Models to generate self-similar traffic. The well-known Bellcore traces are used as a training sequence to learn HMM model parameters. Performance of trained model are tested on the remaining portions of the sequence. Then we can use the HMM trained with the Bellcore data as the traffic source model.01216nas a2200157   4500000000100000000000100001008004100002260005000043100002100093700001900114700002400133700002400157245006900181300001400250520079400264       2014                            d  bSpringer International Publishing Switzerland1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aTadeusz Czachórski00aA few investigations of long-range dependence in network traffic  a137–1443 aThe paper presents measurements and analysis of a LAN long-range dependence traffic collected in IITiS PAN (The Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences). Several methods of Hurst parameter estimation were used, the results obtained by the methods differ substantially. The analysis was made for the whole traffic and traffics generated by particular types of protocols. We seek for a dependence of Hurst parameter on a protocol type. Then, a MMPP(Markov-Modulated Poisson Process) model was applied to mimic the traces. It allows us to consider Markovian queueing models with long-range dependent and self-similar traffic, an important factor as we dispose an efficient software tool to solve numerically very large continuous-time Markov chains.00421nas a2200145   4500000000100000000000100001008004100002260005000043100002100093700001900114700002400133700001400157245009200171300001200263       2015                            d  bSpringer International Publishing Switzerland1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aPiotr Gaj00aEstimating the intensity of long-range dependence in real and synthetic traffic traces.  a11–2200941nas a2200193   4500000000100000000000100001008004100002260001600043100002100059700001900080700002400099700002000123700002300143700002300166700001700189245007900206300001200285520045000297       2013                            d  c1bSpringer1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aAlexander Dudin1 aValentina Klimenok1 aGennadiy Tsarenkov1 aSergey Dudin00aFluid Flow Analysis of RED Algorithm with Modified Weighted Moving Average  a50–583 aWe study with the use of fluid flow approximation the impact of a modified weighted moving average on the performance of RED mechanism. A model of TCP/UDP connection with RED implemented in an intermediate IP router is used, the weighted moving average is determined on the basis of a difference (recursive) equation. The fluid flow approximation technique is applied to model the interactions between the set of TCP/UDP flows and RED mechanism.00398nas a2200145   4500000000100000000000100001008004100002260005100043100002100094700001900115700002400134700002000158700001700178245005700195       2007                            d  bWyższa Szkoła Biznesu w Dąbrowie Górniczej1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aAndrzej Grzywak1 aJerzy Klamka00aPodstawy Budowy i Oprogramowania Sieci Typu Internet00910nas a2200181   4500000000100000000000100001008004100002260005000043100002100093700001900114700002400133700002200157700001400179700001300193245007800206300001200284520043200296       2014                            d  bSpringer International Publishing Switzerland1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aModeling Packet Traffic with the Use of Superpositions of Two-State MMPPs  a24–363 aThe aim of this paper is to use the superposition of two-state Markov Modulated Poisson Processes to replicate the statistical nature of internet traffic over several time scales. This paper characterizes of network traffic using Bellcore data and LAN traces collected in IITiS PAN. The fitting procedure for matching second-order self-similar properties of real data traces to that of two-state MMPP`s has also been described.00622nas a2200133   4500000000100000000000100001008004100002100002100043700001900064700002400083245003200107300001200139520033700151       2012                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aA HMM network traffic model  a17–203 aThe article proposes a description of Internet traffic based on Hidden Markov Model. The model refers to packet level traffic and is validated with the use of real traffic collected in our laboratory. A part of the gathered traces is used as a training sequence to learn HMM model parameters an the rest serves as testing sequences.00327nas a2200145   4500000000100000000000100001000000100002008004100003260000700044100002100051700001900072700002400091245006000115490000600175       2008                            d  c031 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aImplementation of modified AQM mechanisms in IP routers0 v400336nas a2200145   4500000000100000000000100001008004100002100002100043700001900064700002400083245004000107300001200147490000900159022002200168       2007                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aThe Drop-From-Front Strategy in AQM  a61–720 v4712  a978-3-540-74832-800306nas a2200121   4500000000100000000000100001008004100002100002100043700001900064700002400083245006300107300001400170       2005                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aThe influence of traffic self-similarity on QoS Mechanisms  a300–30300353nas a2200145   4500000000100000000000100001000000100002008004100003100002100044700001900065700002400084245007800108300001400186490000700200       2004                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aSelf-similarity of network traffic and the performances of QoS mechanisms  a337–3490 v1600902nas a2200181   4500000000100000000000100001000000100002008004100003100002100044700001900065700002400084700001700108245006700125300001400192490000700206520049300213022001400706       2014                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aJerzy Klamka00aFlluid flow approximation of time-limited TCP/UDP/XCP streams.  a217–2250 v623 aThis article presents the use of fluid flow approximation to model interactions between a set of TCP, UDP and XCP flows in the enveronment of IP routers using AQM (Active Queue Management) algorithms to control traffic congestion. In contrast to other works, independent UDP and TCP streams are considered and the model allows to start and end data transmissions in TCP, UDP and XCP strams at any time moment. It incorporates several Active Queue Management mechanisms: RED, NLRED, CHOKe.  a0239-752800498nas a2200193   4500000000100000000000100001008004100002260004300043100002100086700002200107700002400129700001400153700002000167700002100187700002400208700002500232245004100257490000600298       2006                            d  bWydawnictwo Komunikacji i Łączności1 aJoanna Domańska1 aKrzysztof Grochla1 aTadeusz Czachórski1 aT. Atmaca1 aStefan Węgrzyn1 aLech Znamirowski1 aTadeusz Czachórski1 aStanisław Kozielski00aNowe Technologie Sieci Komputerowych0 v100293nas a2200133   4500000000100000000000100001008004100002100002100043700001600064700001400080700002400094245002700118300001400145       2003                            d1 aJoanna Domańska1 aI. Kotuliak1 aT. Atmaca1 aTadeusz Czachórski00aOptical packet filling  a419–42900918nas a2200181   4500000000100000000000100001008004100002260003500043100001900078700002100097700002400118700002200142700001400164700001300178245011300191300001400304520041800318       2013                            d  c6bSpringeraBerlin Heidelberg1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aComparison of CHOKe and gCHOKe active queues managemant algirithms with the use of fluid flow approximation.  a363–3713 aIn the article we examine a model of TCP connection with Active Queue Managemanet in an intermediate IP router. We model a system where CHOKe or gCHOKe are the AQM policy. We use the fluid flow approximation technique to model the interactions between the set of TCP/UDP flows and two variants of the CHOKe algoithms. The obtained results confirm the superiority of these algorithms over a standard RED algorithm.00787nas a2200193   4500000000100000000000100001008004100002260003200043100001900075700002100094700002400115700002200139700001400161700001300175245002200188490000800210520035300218020002200571       2012                            d  bSpringeraBerlin Heidelberg1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aComputer Networks0 v2913 aIn the article we study a model of TCP/UDP connection with Active Queue Management in an intermediate IP router. We use the fluid flow approximation technique to model the interactions between the set of TCP/UDP flows and AQM. We model a system where RED or NLRED are the AQM policy.The obtained results confirm the advantage of our NLRED algorithm  a978-3-642-31216-800819nas a2200169   4500000000100000000000100001008004100002260008800043100001900131700002100150700002400171700002000195700001800215245008300233520031100316020002200627       2013                            d  bAcademy od Business in Dabrowa Gorniczaa41-300 Dąbrowa Górnicza Cieplaka Str. 1c1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aPiotr Pikiewicz1 aM. Rostański00aInternet in the Information Society Computer Systems Architecture and Security3 aIn the article we study a model of the TCP connection with Active Queue Management in the intermediate bottleneck IP router. The fluid flow approximation technique is used to model the interactions between the set of TCP flows and AQM algorithms. The numerical part of the model is run in CUDA environment.  a978-83-62897-40-700445nas a2200169   4500000000100000000000100001008004100002260004300043100001900086700002100105700002400126700002500150700002200175700002000197700001700217245004100234       2006                            d  bWydawnictwa Komunikacji i Łączności1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aBolesław Pochopień1 aAndrzej Kwiecień1 aAndrzej Grzywak1 aJerzy Klamka00aNowe Technologie Sieci Komputerowych00432nas a2200169   4500000000100000000000100001008004100002260004300043100001900086700002100105700002400126700002000150700002100170700002400191245004100215490000600256       2006                            d  bWydawnictwa Komunikacji i Łączności1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aStefan Węgrzyn1 aLech Znamirowski1 aTadeusz Czachórski00aNowe Technologie Sieci Komputerowych0 v100721nas a2200169   4500000000100000000000100001000000100002008004100003100001900044700002100063700002400084245008800108300001300196490000700209520032100216022001400537       2013                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aGCU-Accelerated fluid flow approximation of the Active Queues Management algorithms  a93–1040 v253 aIn the article we study a model of TCP connection with Active Queue Management in an intermediate IP router. We use the fluid flow approximation technique to model the interactions between the set of TCP flows and AQM algorithms. Computations for fluid flow approximation model are performed in the CUDA environment.  a1896-533400286nas a2200109   4500000000100000000000100001008004100002100001900043700002100062700002400083245006900107       2008                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aThe impact of self-similarity on traffic shaping in wireless LAN00307nas a2200121   4500000000100000000000100001008004100002260006000043100001900103700002100122700002400143245001800167       2005                            d  bOficyna Wydawnicza Politechniki WrocławskiejaWrocław1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aInternet 200500287nas a2200121   4500000000100000000000100001008004100002100001900043700002100062700002400083700001800107245004000125       2008                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aF. Kłębczyk00aSome aspects of QoS in wireless LAN00324nas a2200133   4500000000100000000000100001008004100002260004300043100001900086700002100105700002400126700001800150245002200168       2008                            d  bWydawnictwa Komunikacji i Łączności1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aF. Kłębczyk00aSieci Komputerowe00354nas a2200133   4500000000100000000000100001008004100002100001900043700002100062700002000083700002400103245007000127022002300197       2010                            d1 aAdam Domański1 aJoanna Domańska1 aSławomir Nowak1 aTadeusz Czachórski00aA contribution to the fair scheduling for the TCP and UDP streams  aISBN 3-642-13860-800938nas a2200205   4500000000100000000000100001008004100002260003400043100001700077700002300094700002200117700002200139700002400161700002500185700002200210245005500232300001200287520041100299020002200710       2011                            d  bSpringeraBerlin / Heidelberg1 aPiotr Gawron1 aPrzemysław Głomb1 aJaroslaw Miszczak1 aZbigniew Puchała1 aTadeusz Czachórski1 aStanisław Kozielski1 aUrszula Stańczyk00aEigengestures for natural human computer interface  a49–563 aWe present the application of Principal Component Analysis for data acquired during the design of a natural gesture interface. We investigate the concept of an eigengesture for motion capture hand gesture data and present the visualisation of principal components obtained in the course of conducted experiments. We also show the influence of dimensionality reduction on reconstructed gesture data quality.  a978-3-642-23168-100332nas a2200121   4500000000100000000000100001008004100002260002300043100002200066700002400088700002100112245007700133       2001                            d  bIITiS PANaGliwice1 aKrzysztof Grochla1 aTadeusz Czachórski1 aArkadiusz Sochan00aA tool for network traffic generation and transmission delay measurement00328nas a2200133   4500000000100000000000100001008004100002260001700043100002200060700002400082700001300106700001600119245005900135       2007                            d  c9bIITiS PAN1 aKrzysztof Grochla1 aTadeusz Czachórski1 aA. Busic1 aJ. Fourneau00aSimulation Analysis of Deflection Routing in Hypercube00422nas a2200145   4500000000100000000000100001008004100002260004500043100002200088700002400110700002300134700002100157700002000178245007800198       2004                            d  bWydawnictwa Naukowo-TechniczneaWarszawa1 aKrzysztof Grochla1 aTadeusz Czachórski1 aPrzemysław Głomb1 aArkadiusz Sochan1 aAndrzej Grzywak00aInternet w społeczeństwie informacyjnym - Podstawowe problemy Internetu00293nas a2200109   4500000000100000000000100001008004100002100002200043700002400065700001700089245007700106       2011                            d1 aKrzysztof Grochla1 aTadeusz Czachórski1 aJerzy Klamka00aNumerical Model of Tcp in 802.16 Networks Using Fluid Flow Approximation00330nas a2200145   4500000000100000000000100001000000100002008004100003100001500044700001400059700002400073245006800097300001200165490000700177       2002                            d1 aH. Elbiaze1 aT. Atmaca1 aTadeusz Czachórski00aBlind shaping strategy of self-similar traffic: Markovian model  a13–250 v1401274nas a2200145   4500000000100000000000100001008004100002260003200043100001700075700002400092700002200116245007800138300001400216520089800230       2011                            d  bIEEE Computer Society Press1 aJerzy Klamka1 aTadeusz Czachórski1 aKrzysztof Grochla00aModelling TCP Connection in WIMAX Network Using Fluid Flow Approximation.  a502–5073 aThe fluid-flow approximation models have been during last few years to analyze the dynamics and stability of TCP/RED connections in wired networks. The model assumes that the fluctuations of parameters of the connections are relatively low. The fluid flow models base on the linearization and use of control theory analysis tools to verify the stability and behavior of the analyzed system. In this article we use some extensions of fluid flow approximation to support time division multiplexing used in WiMAX wireless networks. We describe the modification of fluid flow equations to incorporate the delivery of packets as an on-off source with on and off times modulated by the WiMAX scheduling algorithm. The results of the fluid flow model have been compared to the measurement in a small test bed. We also show how to analyze the stability of the connection using control theory approach.00973nas a2200145   4500000000100000000000100001008004100002260010800043100001700151700001600168700002400184245005700208300001200265520055000277       2010                            d  bInstitute of Theoretical and Applied Informatics of the Polish Academy o SciencesaGliwice, Bałtycka 51 aJerzy Klamka1 aJ. Tańcula1 aTadeusz Czachórski00aExamination of robust stability of computer networks  a75–873 aThe thesis presents a nonlinear mathematical model of a computer network with the RED algorithm. The model equations were linearized around the set operating point and next block diagrams of the system presented. Using the methods for examining stability known from the literature, the robust stability of the system has been analysed with slight changes in its parameters. An example of stability examination prepared on the basis of the data for a real computer network has been shown. The results generalise the results known in he literature00855nas a2200169   4500000000100000000000100001008004100002260013300043100002500176700001800201700002100219700002400240700002500264245005200289300001400341520033000355       2014                            d  bSilesian University of Technology, Gliwice, Institute of Theoretical and Applied Informatics, Polish Academy of Science, Gliwice1 aPrzemysław Kowalski1 aDariusz Pojda1 aAleksandra Gruca1 aTadeusz Czachórski1 aStanisław Kozielski00aVisualization of heterogenic images of 3D scene  a291–2973 a<p>The article presents a problem of 3D and 2D heterogenic data visualization. The data come from different devices installed on the robot platform. The data include 3D point cloud, 2D images, 3D and 2D occupancy grids, obtained from laser scanning device, stereocameras and calculated using data from laser 3D scanner.</p>
00889nas a2200157   4500000000100000000000100001008004100002260005000043100001200093700001200105700002400117700001400141245006600155300001400221520049600235       2015                            d  bSpringer International Publishing Switzerland1 aM. Nycz1 aT. Nycz1 aTadeusz Czachórski1 aPiotr Gaj00aAn analysis of the extracted parts of Opte Internet topology.  a371–3813 aThe paper defines a method to extract a part of a graph, which corresponding to a network (Internet) topology covering a certain area. The method refers to data gathered within Opte Project and describing the Internet topology. The extracted parts of graphs have been analyzed in terms of the number of neighbours, longest path length and existence of cycles. Then the resulting topologies are used to model transient behaviour of wide area networks with the use of fluid-flow approximation.00819nas a2200157   4500000000100000000000100001000000100002008004100003100001400044700002400058245007700082300001200159490000700171520046900178022001400647       2012                            d1 aM.M. Nycz1 aTadeusz Czachórski00aModelowanie dynamiki przesyłów TCP/IP z uwzględnieniem mechanizmu RED  a49–620 v333 aW pracy przedstawiono wyniki modelowania wybranych scenariuszy współczesnych sieci komputerowych. W badaniach zastosowano model matematyczny aproksymacji fluid flow, opisujący wpływ zmiennego w czasie natężenia ruchu pakietów na przełączniki sieciowe i generowanie strat pakietowych, zgodnie z założeniami mechanizmu RED stosowanego w routerach. Przeprowadzone doświadczenia obejmowały wpływ początkowych ustawień parametrów na zachowanie układu.  a0208-728600825nas a2200121   4500000000100000000000100001008004100002100001400043700002400057245009500081300001400176520051300190       2011                            d1 aM.M. Nycz1 aTadeusz Czachórski00aModelling transient TCP flows with the use of fluid flow approximation - a numerical study  a181–1843 aThis article presents the problem of modeling the basic elements of the transport system of the modern computer networks. The study of selected issues of the network topology uses a mathematical model of fluid flow approximations, which is based on mean value analysis. The model describes the effects of time varying packet traffic on the network switches and packet loss generation by the assumptions of active queue management mechanisms used in routers. The computations are due to our new software tool.00349nas a2200145   4500000000100000000000100001008004100002260001000043100001800053700002000071700002400091700002200115700002000137245004600157       2008                            d  bWKiŁ1 aMateusz Nowak1 aSławomir Nowak1 aTadeusz Czachórski1 aAndrzej Kwiecień1 aStefan Węgrzyn00aContemporary aspects of computer networks00888nas a2200145   4500000000100000000000100001008004100002260011200043100001800155700001600173700002400189245008300213300001400296520043200310       2010                            d  c1bInstitute of Theoretical and Applied Informatics of the Polish Academy of SciencesaGliwice, Bałtycka 51 aMateusz Nowak1 aPiotr Pecka1 aTadeusz Czachórski00aQoS management for multimedia traffic in synchronous slotted-ring OPS networks  a143–1523 aThe paper presents synchronous slotted-ring network with optical packet switching. The network is equipped with quality of service management, particularly considering multimedia transfers. Thanks to proposed mechanism of guarantees, multimedia data are delivered with guaranteed delay time, not greater than assumed, and in the case the delivery on time is impossible – they are removed, freeing place for remaining packets.00403nas a2200133   4500000000100000000000100001008004100002260003400043100002000077700002000097700002500117700002400142245010300166       2004                            d  bWydawnictwa Naukowo Technicze1 aSławomir Nowak1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aNanotechnologi i nanoinformatyka, Rozdział w książce Współczesne problemy sieci komputerowych00318nas a2200121   4500000000100000000000100001008004100002100002000043700001900063700002100082700002400103245006900127       2010                            d1 aSławomir Nowak1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aSimulation models of fair scheduling for the TCP and UDP streams00297nas a2200121   4500000000100000000000100001008004100002260001700043100002000060700001800080700002400098245005300122       2007                            d  c9bIITiS PAN1 aSławomir Nowak1 aMateusz Nowak1 aTadeusz Czachórski00aParallel simulation of networks with packet loss00344nas a2200145   4500000000100000000000100001008004100002260001000043100002000053700002000073700002000093700002500113700002400138245003600162       2005                            d  bWKiŁ1 aSławomir Nowak1 aStefan Węgrzyn1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWysokowydajne sieci komputerowe01099nas a2200193   4500000000100000000000100001008004100002260003500043100002400078700001200102700001200114700002000126700002200146700001400168700001300182245008200195300001400277520061400291       2013                            d  c6bSpringeraBerlin Heidelberg1 aTadeusz Czachórski1 aM. Nycz1 aT. Nycz1 aFerhan Pekergin1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aAnalytical and numerical means to model transient states in computer networks  a426–4353 aTransient queue analysis is needed to model the influence of time-dependent flows on congestion in computer networks. It may be applied to the networks performance evaluation and the analysis of the transmissions quality of service. However, the exact queuing theory gives us only few practically useful results, concerning mainly M/M/1 and M/M/1/N queues. The article presents potentials of three approaches: Markovian queues solved numerically, the diffusion approximation, and fluid-flow approximation. We mention briefly a software we implemented to use these methods and summarise our experience with it.00930nas a2200193   4500000000100000000000100001008004100002260003300043100001600076700002400092700001800116700002400134700002500158700002200183245003000205490000800235520047100243020002200714       2011                            d  bSpringer Berlin / Heidelberg1 aPiotr Pecka1 aSebastian Deorowicz1 aMateusz Nowak1 aTadeusz Czachórski1 aStanisław Kozielski1 aUrszula Stańczyk00aMan-Machine Interaction 20 v1033 aMarkov chains are used in analysis in many fields. One of them is performance evaluation of computer systems, especially computer networks. For the analysis we use OLYMP object library that provides features to describe complex systems and find their statistical parameters. We present two compressed data structures for the most space-consuming parts of data processed by OLYMP. Then, we show how these improvements move the barrier of applicability of the utility.  a978-3-642-23168-100860nas a2200157   4500000000100000000000100001008004100002260011200043100001600155700001900171700002100190700002400211245007000235300001400305520038300319       2010                            d  c1bInstitute of Theoretical and Applied Informatics of the Polish Academy of SciencesaGliwice, Bałtycka 51 aD. Augustyn1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aActive Queue Management with non-linear packets dropping function  a133–1423 aAbstract: Algorithms of queue management in IP routers determine which packet should be deleted when necessary. The article investigates the influence of packet rejection probability function on the performance, i.e. response time for in case of RED and nRED queues. In particular, the self-similar traffic is considered. The quantitative analysis based on simulations is shown.00288nas a2200121   4500000000100000000000100001008004100002100001600043700002400059700002400083700001400107245004500121       2001                            d1 aR. Laalaoua1 aTadeusz Czachórski1 aStanisław Jędruś1 aT. Atmaca00aDiffusion Model of RED Control Mechanism00339nas a2200145   4500000000100000000000100001000000100002008004100003100001600044700002400060700001400084245007400098300001400172490000700186       2001                            d1 aR. Laalaoua1 aTadeusz Czachórski1 aT. Atmaca00aDiffusion approximation and markovian models of RED control mechanism  a343–3540 v1300244nas a2200109   4500000000100000000000100001008004100002100001600043700002400059700001400083245003700097       2001                            d1 aR. Laalaoua1 aTadeusz Czachórski1 aT. Atmaca00aMarkovian Model of RED Mechanism00901nas a2200169   4500000000100000000000100001008004100002260013300043100002100176700001600197700002100213700002400234700002500258245006500283300001400348520036900362       2014                            d  bSilesian University of Technology, Gliwice, Institute of Theoretical and Applied Informatics, Polish Academy of Science, Gliwice1 aKrzysztof Skabek1 aP. Płoszaj1 aAleksandra Gruca1 aTadeusz Czachórski1 aStanisław Kozielski00aImplementation of registration algorithms for multiple views  a315–3233 aThe article discusses the implementation of algorithms for registration and multiregistration of scans. The multiregistration strategies of mesh sequences for different scanning views were proposed and compared. The comparison criteria comprise parameters of the registration process such as: the accuracy of scan matching, performance, complexity of registration.00365nas a2200145   4500000000100000000000100001008004100002260001000043100002100053700002300074700002000097700002500117700002400142245005300166       2005                            d  bWKiŁ1 aKrzysztof Skabek1 aRyszard Winiarczyk1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWysokowydajne sieci komputerowe Nowe Technologie00271nas a2200097   4500000000100000000000100001008004100002100002500043700002400068245008100092       2005                            d1 aIreneusz Szcześniak1 aTadeusz Czachórski00aOPUS Software for Performance Evaluation of Optical Packet-Switched Networks00414nas a2200145   4500000000100000000000100001008004100002260005300043100002500096700002400121700002000145700002500165700002400190245005400214       2005                            d  bWydawnictwa Komunikacji i ŁącznościaWarszawa1 aIreneusz Szcześniak1 aTadeusz Czachórski1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWysokowydajne sieci komputerowe. Nowe Technologie00311nas a2200109   4500000000100000000000100001008004100002100002500043700002400068700001600092245009300108       2006                            d1 aIreneusz Szcześniak1 aTadeusz Czachórski1 aJ. Fourneau00aPacket Loss Analysis in Optical Packet-Switched Networks with Limited Deflection Routing00360nas a2200121   4500000000100000000000100001008004100002100002500043700001600068700002400084245011600108300001400224       2005                            d1 aIreneusz Szcześniak1 aJ. Fourneau1 aTadeusz Czachórski00aPreliminary results of packet loss analysis in optical packet-switched networks with limited deflection routing  a296–29900933nas a2200193   4500000000100000000000100001008004100002260002500043100001400068700002400082700002200106700001200128700002000140700001700160700001200177245003800189300001400227520049800241       2014                            d  c10bSpringeraLondon1 aT. Atmaca1 aTadeusz Czachórski1 aKrzysztof Grochla1 aT. Nycz1 aFerhan Pekergin1 aErol Gelenbe1 aR. Lent00aA model of boot-up storm dynamics  a371–3793 aThe simultaneous boot of multiple virtual machines or networking devices imposes heavy load on the networking infrastructure. In large network of many virtual machines the coordination of the reboots and device registration is required. In the paper we present a simple analytical model computing the distribution of boot-up time, which is verified by comparison to a simulation model and provide analysis of the influence of several parameters on the overall boot-up time during a boot storm.00300nas a2200145   4500000000100000000000100001000000100002008004100003100001400044700002400058700001600082245003500098300001400133490000700147       2002                            d1 aT. Atmaca1 aTadeusz Czachórski1 aR. Laalaoua00aBadanie dynamiki algorytmu RED  a223–2340 v2301389nas a2200157   4500000000100000000000100001000000100002008004100003100001200044700002400056245014600080300001200226490000700238520097200245022001401217       2012                            d1 aT. Nycz1 aTadeusz Czachórski00aBadanie skalowalności modeli sieci komputerowych wykorzystujących aproksymację dyfuzyjną wraz ze zwiększeniem rozmiaru modelowanej sieci  a63–780 v333 aZmienne natężenie ruchu generowanego przez aplikacje internetowe, np. multimedialne, a także przyjęta przez protokół TCP zasada regulacji natężenia transmisji w funkcji wykrytego obciążenia sieci, określanego na podstawie strat lub czasu transmisji, powodują, że natężenie transmisji internetowych jest permanentnie zmienne w czasie, a zmiany natężenia i dynamika tych zmian muszą być uwzględnione w ocenie algorytmów sterowania ruchem i unikania przeciążeń. Modelowanie struktur w Internecie wymaga z kolei dostosowania istniejących metod i modeli do analizy bardzo dużych konfiguracji sieciowych. Niniejsza praca bada pod kątem obliczeń numerycznych znany model matematyczny, umożliwiający analizę stanów nieustalonych w stanowisku obsługi i ich sieci, wykorzystujący metodę aproksymacji dyfuzyjnej. Zbadano wpływ poszczególnych parametrów modelu i rozmiaru sieci na całkowity czas obliczeń, a więc na skalowalność metody.  a0208-728600921nas a2200181   4500000000100000000000100001008004100002260005000043100001200093700001200105700002400117700002200141700001400163700001300177245012500190300001400315520041000329       2014                            d  bSpringer International Publishing Switzerland1 aT. Nycz1 aM. Nycz1 aTadeusz Czachórski1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aA Numerical Comparison of Diffusion and Fluid-Flow Approximations Used in Modelling Transient States of TCP/IP Networks.  a213–2223 aThe paper presents a comparison of two approaches to queuing models used in performance evaluation of computer networks: diffusion approximation and fluid-flow approximation. Both methods are well known and are used alternatively in similar problems but the differences between their results and the errors they introduce when applied to TCP driven time dependent flows were not sufficiently investigated.00389nas a2200133   4500000000100000000000100001008004100002260004300043100002000086700002000106700002500126700002400151245008000175       2005                            d  bWydawnictwa Komunikacji i Łączności1 aStefan Węgrzyn1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aTechnologie molekularne realizowane przez molekularne systemy informatyczne00400nam a2200157   4500000000100000000000100001008004100002260005900043100002000102700001700122700002200139700002400161245003300185250000600218020001800224       2003                            d  bWydawnictwo Pracowni Komputerowej Jacka Skalmierskiego1 aStefan Węgrzyn1 aJerzy Klamka1 aJaroslaw Miszczak1 aTadeusz Czachórski00aKwantowe systemy informatyki  a2  a83-89105-46-201352nas a2200157   4500000000100000000000100001008004100002260005300043100002100096700002000117700002500137700002400162245005400186520093100240020002301171       2005                            d  bWydawnictwa Komunikacji i ŁącznościaWarszawa1 aLech Znamirowski1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWysokowydajne sieci komputerowe. Nowe technologie3 aW procesach budowy obiektów w dziedzinie informatycznej technologii wytwarzania, jedną z możliwych metod jest wykorzystanie molekularnego programu określającego budowę obiektu, który powstaje na bazie współdziałających, molekularnych narzędzi realizujących program. Analiza działania sieci współpracujących nanostruktur wymaga znajomości konformacji początkowych lub konformacji występujących w sieci w wyniku współdziałania z innymi nanostrukturami. W rozdziale rozpatruje się zagadnienie wyznaczania konformacji dla polipeptydu określonego jego kodem zapisanym w DNA w przypadku powstawania w rybosomie lub w wyniku współdziałania z tzw. białkiem opiekuńczym w fazie innej niż faza powstawania. Przedstawiony został bezgradientowy, dwufazowy algorytm wyznaczania konformacji poprzez obliczenie kątów torsyjnych dla łańcucha aminokwasów. Rozważania zilustrowano przykładem numerycznym.  aISBN 83-206-1573-900380nas a2200145   4500000000100000000000100001008004100002260001800043100002100061700002000082700002500102700002400127245006500151020001800216       2004                            d  bWNTaWarszawa1 aLech Znamirowski1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWspółczesne problemy sieci komputerowych. Nowe technologie  a83-204-3010-000856nas a2200145   4500000000100000000000100001008004100002260003500043100002400078700001700102700001200119245005300131520050400184020002200688       2014                            d  cXbISCIS 29th Annual Symposium1 aTadeusz Czachórski1 aErol Gelenbe1 aR. Lent00aQuantum Queuing Networks Throughput Optimisation3 aWe study models of quantum queues based on discrete time quantum walks with barriers. Our considerations refer to multi-servers queuing models. Input and output of jobs in the queue are realized by systems consisting of quantum coins and walkers. We show that presented models behave differently from the classical ones. We also present numerical methods for optimisation of jobs processed by the system. We simultaneously maximise jobs throughput in the system and minimise the number of jobs lost.  a978-3-319-09464-900342nas a2200121   4500000000100000000000100001008004100002260006800043100002400111700002100135245004600156020001800202       2001                            d  c09bInstytut Informatyki Teoretycznej i Stosowanej PANaGliwice1 aTadeusz Czachórski1 aArkadiusz Sochan00a8th Polish Teletraffic Symposium (Editor)  a83-86644-90-700430nas a2200157   4500000000100000000000100001008004100002260006700043100002400110700001900134700002100153700001600174245006200190300001200252490000800264       2016                            d  bSpringer International Publishing SwitzerlandaBrunów, Poland1 aTadeusz Czachórski1 aAdam Domański1 aJoanna Domańska1 aArtur Rataj00aA Study of IP Router Queues with the Use of Markov Models  a294-3050 v60800577nas a2200205   4500000000100000000000100001008004100002260004400043100001600087700001600103700002400119700002100143700001700164700001700181700001700198245006900215856005100284300001400335020002200349       2016                            d  bSpringer International PublishingaCham1 aMonika Nycz1 aTomasz Nycz1 aTadeusz Czachórski1 aOmer Abdelrahman1 aErol Gelenbe1 aGokce Gorbil1 aRicardo Lent00aModelling Dynamics of TCP Flows in Very Large Network Topologies  uhttp://dx.doi.org/10.1007/978-3-319-22635-4_23  a251–259  a978-3-319-22635-400644nas a2200229   4500000000100000000000100001008004100002260004400043100001700087700002400104700002100128700001800149700001600167700002100183700002300204700002500227700002400252245005500276856005000331300001100381020002200392       2016                            d  bSpringer International PublishingaCham1 aJohan Arcile1 aTadeusz Czachórski1 aJean-Yves Didier1 aHanna Klaudel1 aArtur Rataj1 aAleksandra Gruca1 aAgnieszka Brachman1 aStanisław Kozielski1 aTadeusz Czachórski00aModelling and Analysing Mixed Reality Applications  uhttp://dx.doi.org/10.1007/978-3-319-23437-3_1  a3–17  a978-3-319-23437-300519nas a2200169   4500000000100000000000100001008004100002260004400043100001900087700002100106700002400127245010300151856005100254300001400305490000800319020002200327       2016                            d  bSpringer International PublishingaCham1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aThe Impact of the Degree of Self-Similarity on the NLREDwM Mechanism with Drop from Front Strategy  uhttp://dx.doi.org/10.1007/978-3-319-39207-3_17  a192–2030 v608  a978-3-319-39207-300516nas a2200193   4500000000100000000000100001008004100002260004400043100002400087700001900111700002100130700001900151700001600170245004100186856005100227300001400278490000800292020002200300       2016                            d  bSpringer International PublishingaCham1 aTadeusz Czachórski1 aAdam Domański1 aJoanna Domańska1 aMichele Pagano1 aArtur Rataj00aDelays in IP Routers, a Markov Model  uhttp://dx.doi.org/10.1007/978-3-319-47217-1_20  a185–1920 v659  a978-3-319-47217-100535nas a2200181   4500000000100000000000100001008004100002260004400043100001900087700002100106700001900127700002400146245008800170856005100258300001400309490000800323020002200331       2016                            d  bSpringer International PublishingaCham1 aAdam Domański1 aJoanna Domańska1 aMichele Pagano1 aTadeusz Czachórski00aThe Fluid Flow Approximation of the TCP Vegas and Reno Congestion Control Mechanism  uhttp://dx.doi.org/10.1007/978-3-319-47217-1_21  a193–2000 v659  a978-3-319-47217-100634nas a2200217   4500000000100000000000100001008004100002260004400043100002000087700001800107700002200125700001600147700002400163700001700187700002200204700001700226245008600243856005100329300001400380020002200394       2016                            d  bSpringer International PublishingaCham1 aSławomir Nowak1 aMateusz Nowak1 aKrzysztof Grochla1 aPiotr Pecka1 aTadeusz Czachórski1 aErol Gelenbe1 aKrzysztof Grochla1 aRicardo Lent00aGlobal Queue Pruning Method for Efficient Broadcast in Multihop Wireless Networks  uhttp://dx.doi.org/10.1007/978-3-319-47217-1_23  a214–224  a978-3-319-47217-100372nam a2200169   4500000000100000000000100001000000100002008004100003100002400044700001700068700002200085700001700107245004900124250001300173300000800186490000800194       2016                            d1 aTadeusz Czachórski1 aErol Gelenbe1 aKrzysztof Grochla1 aRicardo Lent00aComputer and Information Sciences ISCIS 2016  aSpringer  a5000 v65901382nas a2200193   4500000000100000000000100001008004100002260003700043100002100080700001700101700002400118700002200142700002300164245009800187300001200285490000800297520086300305020002001168       2018                            d  bSpringeraLondon, United Kingdom1 aJoanna Domańska1 aErol Gelenbe1 aTadeusz Czachórski1 aAnastasios Drosou1 aDimitrios Tzovaras00aResearch and innovation action for the security of the internet of things: The SerIoT project  a101-1180 v8213 a<p>The Internet of Things (IoT) was born in the mid 2010’s, when the threshold of connecting more objects than people to the Internet, was crossed. Thus, attacks and threats on the content and quality of service of the IoT platforms can have economic, energetic and physical security consequences that go way beyond the traditional Internet’s lack of security, and way beyond the threats posed by attacks to mobile telephony. Thus, this paper describes the H2020 project “Secure and Safe Internet of Things” (SerIoT) which will optimize the information security in IoT platforms and networks in a holistic, cross-layered manner (i.e. IoT platforms and devices, honeypots, SDN routers and operator’s controller) in order to offer a secure SerIoT platform that can be used to implement secure IoT platforms and networks anywhere and everywhere.</p>
  a978-331995188-100916nas a2200193   4500000000100000000000100001008004100002260001200043100002100055700001800076700002000094700002400114245005900138856006700197300001200264490000800276520041600284022002200700       2018                            d  c08/20181 aJoanna Domańska1 aMateusz Nowak1 aSławomir Nowak1 aTadeusz Czachórski00aEuropean Cybersecurity Research and the SerIoT Project  uhttps://link.springer.com/chapter/10.1007/978-3-030-00840-6_19  a166-1730 v9353 a<p>This paper briefly reviews some recent research in Cybersecurity in Europe funded by the European Commission in areas such as mobile telephony, networked health systems, the Internet of Things. We then outline the objectives of the SerIoT Project which started in 2018 to address the security needs of fields such as Smart Cities, Smart Transportation Systems, Supply Chains and Industrial Informatics.</p>
  a978-3-030-00839-000416nas a2200157   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001900124245009700143300001000240490000800250       2019                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aJakub Szyguła00aThe AQM Dropping Packet Probability Function Based on Non-integer Order PI^αD^β Controller  a36-480 v49600432nas a2200169   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001800124700001900142245008100161300001200242490000800254       2018                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aDariusz Marek1 aJakub Szyguła00aGPU Accelerated Non-integer Order PI^αD^β Controller Used as AQM Mechanism  a286-2990 v86000465nas a2200169   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001800124700001900142245011600161300001000277490000800287       2018                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aDariusz Marek1 aJakub Szyguła00aThe Influence of the Traffic Self-similarity on the Choice of the Non-integer Order PI^α Controller Parameters  a76-830 v93500373nas a2200145   4500000000100000000000100001008004100002260002200043100001700065700002100082700002400103700002100127700002300148245005600171       2018                            d  bIEEEaRome, Italy1 aErol Gelenbe1 aJoanna Domańska1 aTadeusz Czachórski1 aAnastasis Drosou1 aDimitrios Tzovaras00aSecurity for Internet of Things: The SerIoT Project00380nas a2200145   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107245009100124300001200215490000700227       2016                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka00aThe use of a non-integer order PI controller with an active queue management mechanism  a777-7890 v2600384nas a2200145   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107245009400124300001200218490000800230       2017                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka00aSelf-similarity Traffic and AQM Mechanism Based on Non-integer Order Pi^αD^β Controller  a336-3500 v71800327nas a2200133   4500000000100000000000100001008004100002100002100043700001900064700002400083245006800107300001000175490000800185       2017                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aHidden Markov Models in Long Range Dependence Traffic Modelling  a75-860 v70001564nas a2200109   4500000000100000000000100001008004100002260005100043100002400094245007300118520126300191       2018                            d  c11/2018bRangsit UniversityaBangkok, Thailand1 aTadeusz Czachórski00aModels of traffic control in large Internet topologies, invited talk3 a<p><span style="font-family:times new roman,serif; font-size:12pt">Transient state queueing models help us to understand better the dynamics of Internet transmissions and the performance of traffic control algorithms. Fluid flow approximation, due its simplicity is popular and frequently used -- but even it, if applied to large topologies, is time and space consuming. The alternative approaches as Markov chains or diffusion approximation lead to much more complex computations. Fluid flow approximation is based on first-order differential equations, and its algorithm is based on iterative calculations on large mutually interdependent structures. In consequence, the bottleneck of the method lies not in numerical computations but in storing and selection of data. This is why we use an approach in which a database (SAP HANA) and its language are used to implement the method. The model logic is represented by ETL (Extract, Transform and Load) customizable and user-friendly processes. The numerical examples are based on a real topology having over 100 000 nodes. We may investigate and compare this way various TCP control algorithms as Vegas or Reno, as well as the influence of the introduction of energy aware algorithms to routers.</span></p>
03310nas a2200109   4500000000100000000000100001008004100002260007300043100002400116245011000140520295000250       2018                            d  c11/2018bXi'an University of Science Technology, ChinaaXi'an, China1 aTadeusz Czachórski00aQueueing Models for Performance Evaluation of Computer Networks - Transient State Analysis, invited talk 3 a<p>Queueing theory is a useful tool in design of computer networks and their performance evaluation. The literature concerning this subject is abundant. However, it is in general limited to the analysis of steady states. It means that flows considered in models are constant and obtained solutions do not depend on time. It is in glaring contrast with the flows observed in real networks where the perpetual changes of traffic intensities are due to the nature of users, sending variable quantities of data, cf. multimedia traffic, and also due to the performance of traffic control algorithms which are trying to avoid congestion in networks, e.g. the algorithm of congestion window used in TCP protocol which is adapting the rate of the sent traffic to the observed losses or transmission delays. The quality of Internet transmission services depends on current load of links and not on its average value. Also modelling and understanding the performance of traffic control mechanisms, control stability and its impact on quality of service needs transient state analysis.</p>

<p>We discuss mathematical and computational means used to analyse transient states in queueing models. In computer applications a mathematical model is useful only when it furnishes quantitative results. Therefore practical issues related to numerical side of models are of importance. &nbsp;We present three approaches - Markov models solved numerically, fluid flow approximation and diffusion approximation. A particular importance is given to the latter which is describing the evolution of queues and waiting times in terms of partial differential equations. The author has over 20 year experience in development and application of this method and is also convinced of the qualities of this approach, its flexibility to treat various variants of queueing models.</p>

<p>Fluid flow approximation, due its simplicity is popular and frequently used -- but even it, if applied to large topologies, is time and space consuming. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Fluid flow approximation is based on first-order differential equations, and its algorithm needs iterative calculations on large mutually interdependent structures. In consequence, the bottleneck of the method lies not in numerical computations but in storing and selection of data. This is why we use an approach in which a database and its language are used to implement the method. The numerical examples are based on a real topology having over 100 000 nodes. We may investigate and compare this way various TCP control algorithms as Vegas or Reno, as well as the influence of the introduction of energy aware algorithms to routers.</p>

<p>Traffic intensity observed in computer networks have a complex stochastic nature (self-similarity and long-range dependence) that influences the network performances. We discuss also this side of implemented queueing models.</p>
01579nas a2200109   4500000000100000000000100001008004100002260009000043100002400133245011000157520120200267       2018                            d  c09/2018b Lviv Polytechnic National Universitya Lviv Polytechnic National University1 aTadeusz Czachórski00aQueueing models of traffic control and performance evaluation in large Internet topologies, key-note talk3 a<p>Transient state queueing models help us to understand better the dynamics of internet transmissions and the performance of traffic control algorithms. Fluid flow approximation, due its simplicity is popular and frequently used -- but even it, if applied to large topologies, is time and space consuming. The alternative approaches as Markov chains or diffusion approximation lead to much more complex computations. Fluid flow approximation is based on first-order differential equations, and its algorithm is based on iterative calculations on large mutually interdependent structures. In consequence, the bottleneck of the method lies not in numerical computations but in storing and selection of data. This is why we use an approach in which a database (SAP HANA) and its language are used to implement the method.</p>

<p>The model logic is represented by ETL (Extract, Transform and Load) customizable and user-friendly processes. The numerical examples are based on a real topology having over 100 000 nodes. We may investigate and compare this way various TCP control algorithms as Vegas or Reno, as well as the influence of the introduction of energy aware algorithms to routers.</p>
00284nas a2200097   4500000000100000000000100001008004100002260004500043100002400088245007400112       2018                            d  c09/2018bPolitechnika ŚląskaaZakopane1 aTadeusz Czachórski00aMetoda sterowania natężeniem ruchu w Internecie, referat zaproszony00452nas a2200133   4500000000100000000000100001008004100002260009400043100001800137700002000155700002400175700001600199245010300215       2018                            d  c09/2018bCommunications in Computer and Information Science , vol. 935, SpringeraPoznań1 aGodlove Suila1 aEdelqueen Anyam1 aTadeusz Czachórski1 aArtur Rataj00aPerformance of a buffer between electronic and all-optical networks, diffusion approximation model00385nas a2200121   4500000000100000000000100001008004100002260009300043100001700136700002400153700001600177245007000193       2018                            d  c06/2018bSpringer, Communications in Computer and Information Science, vol. 860aGliwice1 aKuaban Suila1 aTadeusz Czachórski1 aArtur Rataj00aA Queueing Model of the Edge Node in IP over All-Optical Networks00457nas a2200133   4500000000100000000000100001008004100002260010100043100001900144700002400163700001600187245009800203020002200301       2017                            d  c10/2017bSpringer, Advances in Intelligent Systems and Computing, vol. 659, pp. 525-531aKraków1 aAdam Józefiok1 aTadeusz Czachórski1 aArtur Rataj00aA Queueing Model of an Insurance Database Interactive System, Comparison of Modelling Methods  a978-3-319-67791-000448nas a2200133   4500000000100000000000100001008004100002260011100043100001600154700001600170700002400186245007600210020002800286       2017                            d  c09/2017bSpringer, Communications in Computer and Information Science, vol. 700, pp. 49-62aMoscow, Russia1 aMonika Nycz1 aTomasz Nycz1 aTadeusz Czachórski00aPerformance Modelling of Transmissions in Very Large Network Topologies  aISBN: 978-3-319-66835-202624nas a2200157   4500000000100000000000100001000000100002008004100003260006500044100001500109700002400124245011500148250001300263300001200276520217800288       2019                            d  bSpringer, Communications in Computer and Information Science1 aMarek Fiuk1 aTadeusz Czachórski00aA Queueing Model and Performance Analysis of UPnP/HTTP client server interactions in Networked Control Systems  aSpringer  a366-3863 a<div>HTTP based communication protocols are ubiquitous in modern distributed control/supervision systems, therefore it becomes important to understand the performance of the HTTP protocol working with this type of applications and to develop techniques to investigate the impact of the protocol on the overall performance of these systems. A distributed control/supervision system based on HTTP usually consists of multiple nodes sending control requests or measurements to one or more recipient nodes; these data are used to perform control functions, and are stored or presented to supervising personnel. Typically multiple, concurrent flows of data are carried over HTTP to a given recipient. The stochastic characteristics of these flows are often different from the bursty nature of the ordinary Web Browser – Web Server interactions. Furthermore, in order to efficiently carry the randomly arriving data, the number of overlapping (concurrent) HTTP request/response sessions utilized by a sending node is higher than in typical Web Browser -- Web Server communications.</div>

<div>We investigate how the characteristics of nodes participating in the distributed control system communications (number of nodes, service times, number of service stations, queue lengths) influence performance of the employed HTTP protocol. The main focus is on&nbsp; end-to-end delays in transmissions, the most important factor in control. The presented approach can be used to select configuration parameters for the nodes of a realistic control/supervision system having in mind the best communication related performance of such system.</div>

<div>In this work we propose a queuing model of HTTP client/server interactions which we subsequently simplify to be represented by a numerically solvable Markov chain. We compare the results obtained with the simplified model with the output of a discrete event simulation based on the original full model.</div>

<div>The initial motivation for this research came from a study of UPnP based remote PTZ camera control mechanism used by a leading manufacturer of large scale video monitoring systems.</div>

<div>&nbsp;</div>
00439nas a2200157   4500000000100000000000100001008004100002260002000043100001800063700002000081700002100101700002400122245006400146856004900210020002200259       2019                            d  aAarhus, Denmark1 aMateusz Nowak1 aSławomir Nowak1 aJoanna Domańska1 aTadeusz Czachórski00aCognitive Packet Networks for the Secure Internet of Things  uhttps://ieeexplore.ieee.org/document/8766380  a978-1-7281-2171-002334nas a2200853   4500000000100000000000100001008004100002260002100043100001900064700002100083700002300104700002100127700002400148700002500172700002500197700002800222700002700250700002000277700002100297700002500318700001800343700002500361700002100386700002200407700001800429700001300447700001800460700001900478700002100497700001600518700002100534700001800555700002100573700001900594700001900613700002200632700001900654700001900673700001700692700002100709700002300730700001600753700001800769700002300787700002500810700002200835700001900857700002400876700002400900700001900924700001800943700001800961700002000979700002400999700002001023700002201043700002301065700001501088700001701103700002001120700001801140700002401158700002101182700002101203700002301224700001401247700002401261700002001285700001801305700002401323700001901347245009601366020001801462       2018                            d  bRiver Publishers1 aEnrico Ferrera1 aClaudio Pastrone1 aPaul-Emmanuel Brun1 aRemi De Besombes1 aKonstantinos Loupos1 aGerasimos Kouloumpis1 aPatrick O’Sullivan1 aAlexandros Papageorgiou1 aPanayiotis Katsoulakos1 aBill Karakostas1 aAntonis Mygiakis1 aChristina Stratigaki1 aBora Caglayan1 aBasile Starynkevitch1 aChristos Skoufis1 aStelios Christofi1 aNicolas Ferry1 aHui Song1 aArnor Solberg1 aPeter Matthews1 aAntonio Skarmeta1 aJosé Santa1 aMichail Beliatis1 aMirko Presser1 aJosiane Parreira1 aJuan Martínez1 aPayam Barnaghi1 aShirin Enshaeifar1 aThorben Iggena1 aMarten Fischer1 aRalf Tönjes1 aMartin Strohbach1 aAlessandro Sforzin1 aHien Truong1 aJohn Soldatos1 aSofoklis Efremidis1 aGeorgios Koutalieris1 aPanagiotis Gouvas1 aJuergen Neises1 aGeorge Hatzivasilis1 aIoannis Askoxylakis1 aVivek Kulkarni1 aArne Broering1 aDariusz Dober1 aKostas Ramantas1 aChristos Verikoukis1 aJoachim Posegga1 aDomenico Presenza1 aGeorge Spanoudakis1 aDanilo Pau1 aErol Gelenbe1 aSławomir Nowak1 aMateusz Nowak1 aTadeusz Czachórski1 aJoanna Domańska1 aAnastasis Drosou1 aDimitrios Tzovaras1 aTommi Elo1 aSanteri Paavolainen1 aDmitrij Lagutin1 aHelen Leligou1 aPanagiotis Trakadas1 aGeorge Polyzos00aIoT European Security and Privacy Projects: Integration, Architectures and Interoperability  a978877022007100430nas a2200157   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001800124700001900142245010200161490000900263       2019                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aDariusz Marek1 aJakub Szyguła00aAQM Mechanism with the Dropping Packet Function Based on the Answer of Several  PIα  Controllers0 v103900357nas a2200121   4500000000100000000000100001008004100002260000900043100002400052700002200076245007900098856005800177       2019                            d  bIEEE1 aTadeusz Czachórski1 aKrzysztof Grochla00aDiffusion Approximation Models for Cloud Computations with Task Migrations  uhttps://ieeexplore.ieee.org/abstract/document/882182400385nas a2200145   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001900124700001800143245007800161       2020                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aJakub Szyguła1 aDariusz Marek00aDiffusion Approximation Model of TCP NewReno Congestion Control Mechanism00389nas a2200157   4500000000100000000000100001008004100002260002100043100001900064700001900083700002100102700002400123700001800147700001700165245004900182       2020                            d  c23-26 March 20201 aJakub Szyguła1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aDariusz Marek1 aJerzy Klamka00a AQM mechanism with neuron tuning parameters00977nas a2200349   4500000000100000000000100001008004100002260001300043100002200056700001700078700002800095700002200123700003000145700002400175700001700199700002300216700002300239700001500262700002100277700001900298700001600317700001300333700001700346700002100363700002400384700002200408700002000430700001700450700002300467245008900490856004800579       2018                            d  bSpringer1 aAnastasija Collen1 aNiels Nijdam1 aJavier Augusto-Gonzalez1 aSokratis Katsikas1 aKonstantinos Giannoutakis1 aGeorgios Spathoulas1 aErol Gelenbe1 aKonstantinos Votis1 aDimitrios Tzovaras1 aN. Ghavami1 aMelanie Volkamer1 aPiroska Haller1 aA. Sánchez1 aM. Dimas1 aErol Gelenbe1 aPaolo Campegiani1 aTadeusz Czachórski1 aSokratis Katsikas1 aIoannis Komnios1 aLuigi Romano1 aDimitrios Tzovaras00aGHOST - Safe-Guarding Home IoT Environments with Personalised Real-Time Risk Control  uhttps://doi.org/10.1007/978-3-319-95189-8_700480nas a2200169   4500000000100000000000100001008004100002260001300043100002200056700001700078700002400095700001700119700002200136700001700158245008600175856004900261       2018                            d  bSpringer1 aOlumide Akinwande1 aErol Gelenbe1 aTadeusz Czachórski1 aErol Gelenbe1 aKrzysztof Grochla1 aRicardo Lent00aA Reinforcement Learning Approach to Adaptive Forwarding in Named Data Networking  uhttps://doi.org/10.1007/978-3-030-00840-6_2300662nas a2200205   4500000000100000000000100001008004100002260001300043100001700056700002100073700002400094700002200118700002000140700001700160700002300177245018000200856004600380490000800426020002200434       2018                            d  bSpringer1 aErol Gelenbe1 aPaolo Campegiani1 aTadeusz Czachórski1 aSokratis Katsikas1 aIoannis Komnios1 aLuigi Romano1 aDimitrios Tzovaras00aSecurity in Computer and Information Sciences - First International ISCIS Security Workshop 2018, Euro-CYBERSEC 2018, London, UK, February 26-27, 2018, Revised Selected Papers  uhttps://doi.org/10.1007/978-3-319-95189-80 v821  a978-3-319-95188-100514nas a2200157   4500000000100000000000100001008004100002260001300043100002400056700001700080700002200097700001700119245019000136490000800326020002200334       2018                            d  bSpringer1 aTadeusz Czachórski1 aErol Gelenbe1 aKrzysztof Grochla1 aRicardo Lent00aComputer and Information Sciences - 32nd International Symposium, ISCIS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 20-21, 2018, Proceedings0 v935  a978-3-030-00839-001382nas a2200145   4500000000100000000000100001008004100002260002400043100002400067700001700091700001900108700001800127245004400145520104700189       2020                            d  bIEEEaMilano, Italy1 aTadeusz Czachórski1 aErol Gelenbe1 aGodlove Kuaban1 aDariusz Marek00aTransient Behaviour of a Network Router3 a<p>The broader use of Software Defined Network (SDN) controllers creates periodic changes in topology and traffic rates at routers that adapt the network to changes in network conditions.&nbsp;<br />
Thus the transient behaviour of network components, and in particular routers, is becoming of great interest.<br />
Since standard queueing models are difficult to analyze under time-varying conditions, we propose a tractable diffusion approximation for both the transient and steady-state behaviour of a network router. In particular, the analysis provides the steady-state and transient delay and packet loss probability as a function of traffic load and other characteristics. Using these results, we show that when SDN routers change the paths of flows frequently, the network's behaviour may often be far from its steady-state behaviour. Therefore any network optimization conducted with the help of SDN should not be based on&nbsp;steady-state behaviour, but rather on some metric reated to the network time dependent behaviour.</p>
00508nas a2200169   4500000000100000000000100001008004100002260001300043100002400056700001900080700001600099700002500115700002500140700001900165245010600184856004800290       2019                            d  bSpringer1 aTadeusz Czachórski1 aGodlove Kuaban1 aTomasz Nycz1 aVladimir Vishnevskiy1 aKonstantin Samouylov1 aDmitry Kozyrev00aMultichannel Diffusion Approximation Models for the Evaluation of Multichannel Communication Networks  uhttps://doi.org/10.1007/978-3-030-36614-8_400569nas a2200181   4500000000100000000000100001008004100002260001300043100002100056700002400077700002400101700002300125700002500148245013700173856004600310490000900356020002200365       2020                            d  bSpringer1 aAleksandra Gruca1 aTadeusz Czachórski1 aSebastian Deorowicz1 aKatarzyna Harezlak1 aAgnieszka Piotrowska00aMan-Machine Interactions 6 - 6th International Conference on Man-Machine Interactions, ICMMI 2019, Cracow, Poland, October 2-3, 2019  uhttps://doi.org/10.1007/978-3-030-31964-90 v1061  a978-3-030-31963-202537nas a2200097   4500000000100000000000100001008004100002100002400043245023500067520213700302       2019                            d1 aTadeusz Czachórski00aDiffusion approximation as a tool in computer networks performance evaluation, invited talk,  The 9th International Conference on  Electronics, Communications and Networks, CECNet 2019, October 18-21, 2019   Kitakyushu City, Japan3 a<p>Performance of computer networks is frequently investigated with the use of queueing theory. Its models represent the queues of packets waiting in routers to be sent further and evaluate queueing delays, loss probabilities in routers and then the overall transmission quality of service. There is a constant effort &nbsp;to develop models reflecting as exactly as possible the stochastic nature of the transmission intensity, its variability, the distribution of the size of packets, &nbsp;and the rules of traffic management applied by protocols to avoid network congestion. The article describes one of the approaches: diffusion approximation where the size of queues is described by a diffusion process. The method was introduced in 1970-ties by Hisashi Kobayashi and Erol Gelenbe and is still being developed by the author to fit various models of Internet, Internet of Things, Software Defined Networks, Cloud and Fog computing, edge computing. The method is based on the solution of a system of differential partial equations giving&nbsp; an approximation of the queue lengths distribution at any considered time moment. The features that are in favour of the method are:</p>

<ul>
	<li>diffusion model of a single server assumes general&nbsp; interarrival and service time distributions this way going beyond Markov models,</li>
	<li>network models may have any topology, also hierarchical, and any number of nodes (are easy scalable)</li>
	<li>the results are obtained in form of queue distributions and waiting time distributions that makes easier to analyse&nbsp; QoS of paths, e.g. jitter,</li>
	<li>&nbsp;easy separation of each node within a network model,</li>
	<li>the transient state model is solved step-by-stem in small time intervals with parameters specific to these intervals; any decision of a controller concerning&nbsp; dynamic routing of packets,&nbsp; as well as changes of flows due to attacks and control mechanisms may be easily reflected in time-dependent and state-dependent diffusion parameters&nbsp; and time-depended routing probabilities in equations determining the flows.</li>
</ul>
00338nas a2200145   4500000000100000000000100001008004100002100001900043700002100062700002000083700001900103700002400122245003900146490000700185       2020                            d1 aAdam Domański1 aJoanna Domańska1 aKatarzyna Filus1 aJakub Szyguła1 aTadeusz Czachórski00aThe self-similar markovian sources0 v1000358nas a2200133   4500000000100000000000100001008004100002260003000043100001900073700002400092700001600116245007100132020002100203       2018                            d  bSpringeraGliwice, Poland1 aGodlove Kuaban1 aTadeusz Czachórski1 aArtur Rataj00aA Queueing Model of the Edge Node in IP over  All-Optical Networks  a978-3-3199-249-500425nas a2200145   4500000000100000000000100001008004100002260003000043100001900073700002000092700002400112700001600136245010400152020002300256       2018                            d  bSpringeraPoznań, Poland1 aGodlove Kuaban1 aEdelqueen Anyam1 aTadeusz Czachórski1 aArtur Rataj00aPerformance of a buffer between  electronic and all-optical networks, diffusion approximation model  a 978-3-0300-0839-000445nas a2200169   4500000000100000000000100001008004100002100001900043700001900062700002100081700002400102700001700126700001800143245004900161856005800210490000700268       2021                            d1 aJakub Szyguła1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aDariusz Marek00aThe IoT Gateway with Active Queue Management  uhttps://www.amcs.uz.zgora.pl/?action=paper&paper=15980 v3101417nas a2200205   4500000000100000000000100001008004100002260001200043100001800055700001900073700002100092700002400113700001700137700001900154245008700173856007100260490000800331520085800339022001401197       2021                            d  c01/20211 aDariusz Marek1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aJakub Szyguła00aCombined diffusion approximation - simulation model of AQM’s transient behaviour  uhttp://www.sciencedirect.com/science/article/pii/S01403664203199400 v1663 a<p>The article introduces an approach combining diffusion approximation and simulation ones. Furthermore, it describes how it can be used to evaluate active queue management (AQM) mechanisms. Based on the obtained queue distributions, the simulation part of the model decides on package losses and modifies the flow intensity sent by the transmitter. The diffusion is used to estimate queue distributions and the goal of the simulation part of the model is to represent the AQM mechanism. On the one hand, the use of the diffusion part considerably accelerates the performance of the whole model. On the other hand, the simulation increases the accuracy of the diffusion part. We apply the model to compare the performance of fractional order PIη controller used in AQM with the performance of RED, a well known active queue management mechanism.</p>
  a0140-366401554nas a2200193   4500000000100000000000100001008004100002260005600043100002400099700001700123700001900140700001800159245008100177250003000258856003300288300001000321490000900331520102000340       2020                            d  c12/2020bCEUR-WS.orgaPetrozavodsk, Karelia, Russia1 aTadeusz Czachórski1 aErol Gelenbe1 aGodlove Kuaban1 aDariusz Marek00aTime Dependent Diffusion Model for Security Driven Software Defined Networks  aCEUR Workshop Proceedings  uhttp://ceur-ws.org/Vol-2792/  a38-560 v27923 a<p>We present a model of a Software Defined Network (SDN) where frequent changes in routing and traffic rates at routers are needed to respond to the security, quality of service (QoS), and energy savings requirements of applications such as the Internet of Things.<br />
Such frequent path and traffic changes introduce time-dependent network behaviours, and standard queueing models are not well adapted to analyse the transient regime, we propose a tractable diffusion approximation for both the transient and steady-state behaviour.<br />
Our model can represent any network topology transmitting time-dependent flows with routing changes, and computes queue length and delay distributions at each network node and along&nbsp;complete paths between senders and receivers.&nbsp;<br />
Using realistic router parameters, we show that transients occupy a significant fraction of system time, so that the optimisation conducted with SDN controllers needs to include the effect of time-dependent behaviours.</p>
02557nas a2200181   4500000000100000000000100001008004100002260005600043100001700099700001600116700001900132700002400151245009600175250006500271490001000336520200700346020002202353       2020                            d  c12/2020bSpringer International Publishingaon-line1 aTulin Atmaca1 aAmira Kamli1 aGodlove Kuaban1 aTadeusz Czachórski00aPerformance Evaluation of the Packet Aggregation Mechanism of an N-GREEN Metro Network Node  aComputer Communication Networks and Telecommunications, LNCS0 v125273 a<p>Today's telecommunication network infrastructure and services are dramatically changing due partially to the rapid increase in the amount of traffic generation and its transportation. This rapid change is also caused by the increased demand for a high quality of services and the recent interest in green networking strengthened by cutting down carbon emission and operation cost. Access networks generate short electronic packets of different sizes, which are aggregated into larger optical packets at the ingress edge nodes of the optical backbone network. It is transported transparently in the optical domain, reconverted into the electronic domain at the egress edge nodes, and delivered to the destination access networks. Packet aggregation provides many benefits at the level of MAN, and core networks such as, increased spectral efficiency, energy efficiency, optimal resource utilisation, simplified traffic management which significantly reduces protocol and signalling overhead. However, packet aggregation introduces performance bottleneck at the edge node as the packets from the access networks are temporarily stored in the aggregation buffers during the packet aggregation process. In this article, we apply the diffusion approximation model and other stochastic modelling methods to analytically evaluate the performance of a new packet aggregation mechanism which was developed specifically for an N-GREEN (Next Generation of Routers for Energy &nbsp;Efficiency) metro network. We obtain the distribution of the packets' queue in the aggregation buffer, which influences the distribution of the waiting time (delay) experienced by packets in the aggregation buffer. We then, demonstrate the influence of the probability $p$ of successfully inserting the packet data units from the aggregation queue to the optical ring within a defined timeslot $\Delta$. We also discuss the performance evaluation of the complete ring by deriving the utilisation of each link.<br />
&nbsp;</p>
  a978-3-030-68110-401417nas a2200181   4500000000100000000000100001008004100002260005600043100002400099700001700123700001800140245005300158250006500211856005100276490001000327520087600337020002201213       2020                            d  c12/2020bSpringer International Publishingaon-line1 aTadeusz Czachórski1 aErol Gelenbe1 aDariusz Marek00aSoftware Defined Network Dynamics via Diffusions  aComputer Communication Networks and Telecommunications, LNCS  uhttps://www.springer.com/gp/book/97830306810980 v125273 a<p>Software-Defined Networks (SDN) dynamically modify the paths of Internet flows in response to the quality of service or security needs, and hence frequently modify traffic levels at network routers. Thus network routers often operate in the transient regime, rather than at steady-state, with significant impact on packet loss probabilities and delay. We, therefore, investigate the time-dependent performance of a small network of routers, modelled as G/G/1/N queueing stations. &nbsp;A diffusion approximation is developed to predict the quality of service of the routers in the transient regime. Numerical examples show that&nbsp;the results in the transient regime can differ very significantly from the steady-state results, and therefore that the&nbsp;transient analysis must be taken into account in evaluating the performance of routers in a SDN network.</p>
  a978-3-030-68110-401378nas a2200157   4500000000100000000000100001008004100002100002400043700001700067700001900084700001800103245008100121300001400202490000900216520099500225       2020                            d1 aTadeusz Czachórski1 aErol Gelenbe1 aGodlove Kuaban1 aDariusz Marek00aTime Dependent Diffusion Model for Security Driven Software Defined Networks  app. 38-560 v27923 a<p>We present a model of a Software Defined Network (SDN) where frequent changes in routing and traffic rates at routers are needed<br />
to respond to the security, quality of service (QoS), and energy savings requirements of applications such as the Internet of Things. Such frequent path and traffic changes introduce time-dependent network behaviours, and standard queueing models are not well adapted to analyse the transient regime, we propose a tractable diffusion approximation for both the transient and steady-state behaviour. Our model can represent any network topology transmitting time-dependent flows with routing changes, and computes queue length and delay distributions at each network node and along complete paths between senders and receivers. Using realistic router parameters, we show that transients occupy a significant fraction of system time, so that the optimisation conducted with SDN controllers needs to include the effect of time-dependent behaviours.</p>
01671nas a2200169   4500000000100000000000100001008004100002260001200043100002400055700001700079700001900096700001800115245007100133856004100204490000700245520124900252       2021                            d  c03/20211 aTadeusz Czachórski1 aErol Gelenbe1 aGodlove Kuaban1 aDariusz Marek00aTime-Dependent Performance of a Multi-Hop Software Defined Network  uhttps://www.mdpi.com/journal/applsci0 v113 a<p>It has been recently observed that Software Defined Networks (SDN) can change the<br />
paths of different connections in the network at a relatively frequent pace to improve the overall<br />
network performance, including delay and packet loss, or to respond to other needs such as security.<br />
These changes mean that a network that SDN controls will seldom operate in steady state; rather,<br />
the network may often be in transient mode, especially when the network is heavily loaded and<br />
path changes are critically important. Hence, we propose a transient analysis of such networks to<br />
better understand how frequent changes in paths and the switches’ workloads may affect multi-hop<br />
networks’ performance. Since conventional queueing models are difficult to solve for transient<br />
behaviour and simulations take excessive computation time due to the need for statistical accuracy,<br />
we use a diffusion approximation to study a multi-hop network controlled by SDN. The results show<br />
that network optimization should consider the transient effects of SDN and that transients need to be<br />
included in the design of algorithms for SDN controllers that optimize network performance.</p>
00431nas a2200169   4500000000100000000000100001008004100002260001200043100001800055700001900073700002100092700001900113700002400132700001700156245008100173490000700254       2021                            d  c05/20211 aDariusz Marek1 aAdam Domański1 aJoanna Domańska1 aJakub Szyguła1 aTadeusz Czachórski1 aJerzy Klamka00aDiffusion model of a non-integer order PIγ controller with TCP/UDP  streams0 v2301535nas a2200145   4500000000100000000000100001008004100002260003900043100001700082700001500099700001800114700002400132245009800156520113500254       2021                            d  c11/2021bIEEEaHouston, Texas, USA1 aErol Gelenbe1 aMert Nakip1 aDariusz Marek1 aTadeusz Czachórski00aDiffusion Analysis Improves Scalability of IoTNetworks to Mitigate the Massive Access Problem3 a<p>A significant challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from a massive number of IoT devices are forwarded to an IoT gateway. Many IoT devices tend to report their data to their wired or wireless network gateways at closely correlated instants of time, leading to congestion known as the MassiveAccess Problem (MAP), which increases the probability that the IoT data will not meet its required deadlines. Since IoT data loses much of its value if it arrives to destination beyond a required deadline, MAP has been extensively studied in the literature. Thus we first take a queueing theoretic view of the problem, and also use a Diffusion Approximation to gain insight into the IoT traffic statistics that affect MAP. Then we introduce the Quasi-Deterministic Transmission Policy (QDTP) which significantly alleviates MAP when the average traffic rate grows beyond a given level and substantially reduces the probability that IoT data deadlines are missed. The results are validated using real IoT data which has been placed in IP packets for transmission.</p>
01085nas a2200145   4500000000100000000000100001008004100002260001200043100001600055700002400071700001600095245012100111490000700232520070000239       2021                            d  c07/20211 aTomasz Nycz1 aTadeusz Czachórski1 aMonika Nycz00aDiffusion Model of Preemptive-Resume Priority Systems and Its Application to Performance Evaluation of SDN Switches.0 v213 a<p>The increasing use of Software-Defined Networks brings the need for their performance analysis and detailed analytical and numerical models of them. The primary element of such research is a model of a SDN switch. This model should take into account non-Poisson traffic and general distributions of service times. Because of frequent changes in SDN flows, it should also analyze transient states of the queues. The method of diffusion approximation can meet these requirements. We present here a diffusion approximation of priority queues and apply it to build a more detailed model of SDN switch where packets returned by the central controller have higher priority than other packets.</p>
01800nas a2200169   4500000000100000000000100001008004100002260001200043100001900055700001700074700001600091700002400107700002000131245014200151490000700293520133000300       2021                            d  c06/20211 aGodlove Kuaban1 aTulin Atmaca1 aAmira Kamli1 aTadeusz Czachórski1 aPiotr Czekalski00aPerformance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks0 v213 aThe transmission of massive amounts of small packets generated by access networks
through high-speed Internet core networks to other access networks or cloud computing data centres has introduced several challenges such as poor throughput, underutilisation of network resources,  and higher energy consumption. Therefore, it is essential to develop strategies to deal with these challenges. 
One of them is to aggregate smaller packets into a larger payload packet, and these groups of aggregated packets will share the same header, hence increasing throughput, improving resource utilisation, and reduction in energy consumption. This paper presents a review of the packet aggregation applications in access networks (e.g., IoT and 4G/5G mobile networks), optical core networks, and cloud computing data centre networks. 
Then we propose new analytical models based on diffusion approximation for the evaluation of the performance of packet aggregation mechanisms. We demonstrate the use of measured traffic from real networks to evaluate the performance of packet aggregation mechanisms analytically. The use of diffusion approximation allows us to consider time-dependent queueing models with general interarrival and service time distributions. Therefore these models are more general than others presented till now.
02934nas a2200145   4500000000100000000000100001008004100002260004200043100001700085700001600102700001900118700002400137245009600161520253100257       2021                            d  c January 2021bSpringeraNice, France1 aTulin Atmaca1 aAmira Kamli1 aGodlove Kuaban1 aTadeusz Czachórski00aPerformance Evaluation of the Packet Aggregation Mechanism of an N-GREEN Metro Network Node3 a<p><span style="background-color:rgb(255, 255, 255); color:rgb(51, 51, 51); font-family:hind,arial,sans-serif; font-size:15px">Today’s telecommunication network infrastructure and services are dramatically changing due partially to the rapid increase in the amount of traffic generation and its transportation. This rapid change is also caused by the increased demand for a high quality of services and the recent interest in green networking strengthened by cutting down carbon emission and operation cost. Access networks generate short electronic packets of different sizes, which are aggregated into larger optical packets at the ingress edge nodes of the optical backbone network. It is transported transparently in the optical domain, reconverted into the electronic domain at the egress edge nodes, and delivered to the destination access networks. Packet aggregation provides many benefits at the level of MAN, and core networks such as, increased spectral efficiency, energy efficiency, optimal resource utilisation, simplified traffic management which significantly reduces protocol and signalling overhead. However, packet aggregation introduces performance bottleneck at the edge node as the packets from the access networks are temporarily stored in the aggregation buffers during the packet aggregation process. In this article, we apply the diffusion approximation model and other stochastic modelling methods to analytically evaluate the performance of a new packet aggregation mechanism which was developed specifically for an N-GREEN (Next Generation of Routers for Energy Efficiency) metro network. We obtain the distribution of the packets’ queue in the aggregation buffer, which influences the distribution of the waiting time (delay) experienced by packets in the aggregation buffer. We then, demonstrate the influence of the probability&nbsp;</span><em>p</em><span style="background-color:rgb(255, 255, 255); color:rgb(51, 51, 51); font-family:hind,arial,sans-serif; font-size:15px">&nbsp;of successfully inserting the packet data units from the aggregation queue to the optical ring within a defined timeslot&nbsp;</span><span style="background-color:rgb(255, 255, 255); color:rgb(51, 51, 51); font-family:hind,arial,sans-serif; font-size:15px">Δ</span><span style="background-color:rgb(255, 255, 255); color:rgb(51, 51, 51); font-family:hind,arial,sans-serif; font-size:15px">. We also discuss the performance evaluation of the complete ring by deriving the utilisation of each link.</span></p>
00491nas a2200193   4500000000100000000000100001000000100002008004100003260004800044100002200092700002400114700001700138700002200155700001800177245006400195300000800259490000800267020002200275       2021                            d  bSpringeraVirtual Event, June 28–29, 20211 aAleksander Byrski1 aTadeusz Czachórski1 aErol Gelenbe1 aKrzysztof Grochla1 aYuko Murayama00aComputer Science Protecting Human Society Against Epidemics  a1250 v616  a978-3-030-86582-501329nas a2200169   4500000000100000000000100001008004100002260001200043100001600055700001600071700002400087245007500111856004500186490000600231520090800237022001401145       2021                            d  c12/20211 aMonika Nycz1 aTomasz Nycz1 aTadeusz Czachórski00aFluid-Flow Approximation in the Analysis of Vast Energy-Aware Networks  uhttps://www.mdpi.com/2227-7390/9/24/32790 v93 a<p>The paper addresses two issues: (i) modeling dynamic flows transmitted in vast TCP/IP networks and (ii) modeling the impact of energy-saving algorithms. The approach is based on the fluid-flow approximation, which applies first-order differential equations to analyze the evolution of queues and flows. We demonstrate that the effective implementation of this method overcomes the constraints of storing large data in numerical solutions of transient problems in vast network topologies. The model is implemented and executed directly in a database system. It can analyze transient states in topologies of more than 100,000 nodes, i.e., the size which was not considered until now. We use it to investigate the impact of an energy-saving algorithm on the performance of a vast network. We find that it reduces network congestion and save energy costs but significantly lower network throughput.</p>
  a2227-739000511nas a2200181   4500000000100000000000100001008004100002100001800043700001900061700002100080700001900101700002400120700001700144700002000161245006400181856007700245490000700322       2022                            d1 aDariusz Marek1 aAdam Domański1 aJoanna Domańska1 aJakub Szyguła1 aTadeusz Czachórski1 aJerzy Klamka1 aKatarzyna Filus00aApproximation Models for the Evaluation of TCP/AQM Networks  uhttps://journals.pan.pl/dlibra/publication/141986/edition/123961/content0 v7001328nas a2200145   4500000000100000000000100001008004100002260002700043100001700070700001500087700001800102700002400120245006800144520097000212       2022                            d  bSpringeraNice, France1 aErol Gelenbe1 aMert Nakip1 aDariusz Marek1 aTadeusz Czachórski00aMitigating the Massive Access Problem in the Internet of Things3 a<p>The traffic from the large number of IoT devices connected to the IoT is a source of congestion known as the Massive Access Problem (MAP), that results in packet losses, delays and missed deadlines for realtime data. This paper reviews the literature on MAP and summarizes recent results on two approaches that have been designed to mitigate MAP. One approach is based on randomizing the packet arrival instants to IoT gateways within a given time interval that is chosen so that packet arrivals do not exceed their deadlines, but also so that they do not constitute bulk arrivals. The second approach is a novel traffic shaping policy named the Quasi-Deterministic-Transmission-Policy (QDTP) which has been proved to drastically reduce queue formation at the receiving gateway by delaying packet departures from the IoT devices in a judicious manner. Both analytical and experimental results are summarized, that describe the benefits of these techniques.</p>
00999nas a2200133   4500000000100000000000100001008004100002260001200043100001700055700001500072700002400087245004500111520070900156       2022                            d  c08/20221 aErol Gelenbe1 aMert Nakip1 aTadeusz Czachórski00aImproving Massive Access to IoT Gateways3 a<p>IoT networks handle incoming packets from large numbers of IoT Devices (IoTDs) to IoT Gateways. This can lead to the IoT Massive Access Problem that causes buffer overflow, large end-to-end delays and missed deadlines. This paper analyzes a novel traffic shaping method named the Quasi-Deterministic Traffic Policy (QDTP) that mitigates this problem by shaping the incoming traffic without increasing the end-to-end delay or dropping packets. Using queueing theoretic techniques and extensive data driven simulations with real IoT datasets, the value of QDTP is shown as a means to considerably reduce congestion at the Gateway, and significantly improve the IoT network’s overall performance.</p>
01281nas a2200157   4500000000100000000000100001008004100002260002700043100002400070700001700094700001900111700001800130245005000148490000900198520091600207       2022                            d  bSpringeraNice, France1 aTadeusz Czachórski1 aErol Gelenbe1 aGodlove Kuaban1 aDariusz Marek00aOptimizing Energy Usage for an Electric Drone0 v15963 a<p>Unmanned Aerial Vehicles (UAVs) are rapidly gaining popu-<br />
larity in a wide variety of applications, e.g., agriculture, health care, envi-<br />
ronmental management, supply chains, law enforcement, surveillance,<br />
and photography. Dones are often powered by batteries, making energy<br />
a critical resource that must be optimised during the mission of the<br />
drone. The duration of a done’s mission depends on the amount of energy<br />
required to perform some manoeuvering actions (takeoff, level flight, hov-<br />
ering, and landing), the energy required to power the ICT modules in<br />
the drone, the drone’s speed, payload, and the wind. In this paper, we<br />
present a model that minimizes the energy consumption of a low power<br />
drone and maximizes the time required to completely drain the drone’s<br />
battery and ensure the safe landing of the drone.</p>
01364nas a2200133   4500000000100000000000100001008004100002260002300043100002400066700001700090700001900107245007900126520102500205       2022                            d  bIEEEaNice, France1 aTadeusz Czachórski1 aErol Gelenbe1 aGodlove Kuaban00aModelling energy changes in the energy harvesting battery of an IoT device3 a<p>The complexity of battery-powered autonomous de-<br />
vices such as Internet of Things (IoT) nodes or Unmanned Aerial<br />
Vehicles (UAV) and the necessity to ensure an acceptable quality<br />
of service, reliability, and security, have significantly increased<br />
their energy demand. In this paper, we discuss using a diffusion<br />
approximation process to approximate the dynamic changes in<br />
the energy content of a battery. We consider the case when<br />
energy harvesting sources are constantly charging the battery.<br />
The model assumes a probabilistic consumption and delivery of<br />
energy, giving the time-dependent distributions of the energy at<br />
the battery, of the time remaining until it becomes empty, the<br />
time required to charge the battery to its total capacity, or the<br />
time it is operational between two moments of complete depletion.<br />
When possible, we compare the diffusion approximation results<br />
with corresponding Markovian models.</p>
01293nas a2200133   4500000000100000000000100001008004100002260003900043100002400082700001700106700001900123245007900142520093800221       2023                            d  c03/2023bIEEE XploreaNice, France1 aTadeusz Czachórski1 aErol Gelenbe1 aGodlove Kuaban00aModelling energy changes in the energy harvesting battery of an IoT device3 a<p>The complexity of battery-powered autonomous devices such as Internet of Things (IoT) nodes or Unmanned Aerial Vehicles (UAV) and the necessity to ensure an acceptable quality of service, reliability, and security have significantly increased their energy demand. In this paper, we discuss using a diffusion approximation process to approximate the dynamic changes in the energy content of a battery. We consider the case when energy harvesting sources are constantly charging the battery. The model assumes a probabilistic consumption and delivery of energy, giving the time-dependent distributions of the energy at the battery, of the time remaining until it becomes empty, the<br />
time required to charge the battery to its total capacity, or the time it is operational between two moments of complete depletion.<br />
When possible, we compare the diffusion approximation results with corresponding Markovian models.</p>
01276nas a2200193   4500000000100000000000100001000000100002008004100003260002700044100002400071700001700095700001900112700001800131245005000149300001200199490000900211520084000220020002201060       2022                            d  bSpringeraNice, France1 aTadeusz Czachórski1 aErol Gelenbe1 aGodlove Kuaban1 aDariusz Marek00aOptimizing Energy Usage for an Electric Drone  a61–750 v15963 a<p>Unmanned Aerial Vehicles (UAVs) are rapidly gaining popularity in a wide variety of applications, e.g., agriculture, health care, environmental management, supply chains, law enforcement, surveillance, and photography. Dones are often powered by batteries, making energy<br />
a critical resource that must be optimised during the mission of the drone. The duration of a done’s mission depends on the amount of energy required to perform some manoeuvering actions (takeoff, level flight, hovering, and landing), the energy required to power the ICT modules in the drone, the drone’s speed, payload, and the wind. In this paper, we present a model that minimizes the energy consumption of a low-power drone and maximizes the time required to completely drain the drone’s battery and ensure the safe landing of the drone.</p>
  a978-3-031-09357-902144nas a2200169   4500000000100000000000100001008004100002260001200043100001900055700001700074700002400091700002000115700001800135245012900153490000700282520168500289       2023                            d  c07/20231 aGodlove Kuaban1 aErol Gelenbe1 aTadeusz Czachórski1 aPiotr Czekalski1 aJulius Tangka00aModelling of the Energy Depletion Process and Battery Depletion Attacks for Battery-Powered Internet of Things (IoT) Devices0 v233 a<pre>
The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device's battery should be replaced in order to ensure that the battery is not completely drained before it is replaced. </pre>
01685nas a2200145   4500000000100000000000100001008004100002260004300043100001900086700001700105700002400122700002000146245009000166520128300256       2024                            d  c16/01/2024bIEEEaStony Brook, NY, USA1 aGodlove Kuaban1 aErol Gelenbe1 aTadeusz Czachórski1 aPiotr Czekalski00aModelling the Energy Performance of Off-Grid Sustainable Green Cellular Base Stations3 a<pre>
There is a growing awareness of the need to reduce carbon emissions from the operation of mobile networks. The massive deployment of ultra-dense 5G and IoT networks will significantly increase energy demand and put the electricity grid under stress while also driving up operational costs. In this paper, we model the energy performance of an off-grid sustainable green cellular base station site which consists of a solar power system, Battery Energy Storage (BESS) and Hydrogen Energy Storage (HESS) system, and various types of macrocells, microcells, picocells, or femtocells, with broadband optical or microwave transmission systems, and other electrical and electronic systems (air conditioner, power converters, and controllers. We propose diffusion-based models of the charging and discharging processes of the energy storage systems, and obtain the probability of charging them to their full capacities during the day and completely discharging them at the end of each day. We also investigate the impact of design parameters such as the mean charging rate and the mean discharging rate on the probability densities of charging BESS and HESS to their full capacities during the day and of completely discharging them before the end of each night period. </pre>
01666nas a2200169   4500000000100000000000100001008004100002260000900043100001900052700002400071700001700095700001800112700002000130245009500150520122900245020002201474       2024                            d  bIEEE1 aGodlove Kuaban1 aTadeusz Czachórski1 aErol Gelenbe1 aSapana Sharma1 aPiotr Czekalski00aA Markov model for a Self-Powered Green IoT Device with State-Dependent Energy Consumption3 a<pre>
The growing concerns about the resource sustainability and environmental impact of deploying billions or trillions of IoT devices in the various sectors of society or economy have given rise to an energy research field known as Green-IoT (G-IoT). G-IoT emphasises the need to prioritise energy efficiency and the reduction of pollution in the IoT ecosystem. This paper presents a Markov model for a self-powered Green IoT device with state-dependent energy consumption. The model evaluates the energy performance of a self-powered Green IoT device. We derive the probability distribution of the energy content of the battery, including the probability that the energy stored in the battery is completely depleted and the density of the lifetime of the IoT device. We investigate the influence of some design parameters on the results. Introducing the threshold at which the device is forced to enter an energy-saving regime significantly increases a G-IoT device's lifetime. Apart from deploying energy-efficient mechanisms to increase the lifetime of a G-IoT device, integrating an efficient energy harvesting system into the device will increase its lifetime and reduce the frequency of battery replacement.</pre>
  a979-8-3503-3691-702099nas a2200181   4500000000100000000000100001008004100002260004000043100001900083700002400102700001700126700001600143700001900159700002000178245008400198520161300282020002201895       2024                            d  c17/07/2024bIEEEaAyia Napa, Cyprus1 aGodlove Kuaban1 aTadeusz Czachórski1 aErol Gelenbe1 aPiotr Pecka1 aValery Nkemeni1 aPiotr Czekalski00aEnergy performance of Internet of Things (IoT) networks for pipeline monitoring3 a<pre>
Pipelines are the most convenient ways to transport fluids (e.g., water, oil, and gas). However, leakage of fluids into the environment results in resource wastage (especially water, which is becoming a scarce resource) and environmental pollution (in the case of leakage of toxic fluids like oil and gas). Emerging technologies like the Internet of Things (IoT), Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), distributed computing, and cloud computing enable continuous monitoring of pipelines to detect leakages and corrosion on the pipeline. The main challenge with using battery-powered sensor nodes to monitor pipelines is the energy constraint, necessitating frequent battery replacement. Thus, there is a need to develop energy-saving mechanisms to prolong the lifetime of these sensor nodes. In this paper, we use the diffusion approximation modelling framework in which the data from the experimental testbed are used to model the dynamics of the battery's energy content and to estimate the mean and variance of the device's lifetime. The novelty in the proposed diffusion model of the battery of an IoT node is the introduction of multiple energy thresholds that split the energy state-space of the battery into multiple energy-saving regimes. As the battery discharges, the node gradually transitions into energy-saving regimes by reconfiguring some of its parameters to reduce energy consumption (sometimes at the cost of trading off some performance metrics). We investigate the impact of energy-saving regimes or the number of thresholds on the node's lifetime.</pre>
  a979-8-3503-6126-101486nas a2200169   4500000000100000000000100001008004100002260001500043100001900058700001700077700002400094700002000118700001900138245006400157490000800221520108700229       2024                            d  c10/07/20241 aGodlove Kuaban1 aErol Gelenbe1 aTadeusz Czachórski1 aPiotr Czekalski1 aValery Nkemeni00aEnergy Performance of Off-Grid Green Cellular Base Stations0 v1653 a<pre>
The most energy-hungry parts of mobile networks are the base station sites, which consume around $60-80\%$ of their total energy. One of the approaches for relieving this energy pressure on the electricity grid infrastructure and reducing the Operational Expenditures (OPEX) is to power base stations with renewable energy. However, the design of a green mobile network requires the dimensioning of the energy harvesting and storage systems through the estimation of the network's energy demand. Therefore, this paper develops a diffusion-based modelling framework for solar-powered green off-grid base station sites. We apply this framework to evaluate the energy performance of homogeneous and hybrid energy storage systems supplied by harvested solar energy. We present the complete analysis, with numerical examples, to study the relationship between the design parameters and the energy performance metrics. The numerical computations demonstrate how the proposed framework can be applied to evaluate homogeneous and unconventional hybrid energy storage systems.</pre>
01587nas a2200145   4500000000100000000000100001008004100002100001900043700002400062700002000086245014600106856012400252520104300376020002201419       2024                            d1 aGodlove Kuaban1 aTadeusz Czachórski1 aPiotr Czekalski00aMarkov Models for Dimensioning and Provisioning of Battery Energy Storage Systems (BESS) for Off-Grid Green Mobile Network Base Station Sites  uchrome-extehttps://icmss.nust.na/sites/default/files/ICMSS-1%20Conference%20Proceedings_1st%20Edition_Final-11apr24.pdf3 a<pre>
There is a growing desire by mobile network operators and other stakeholders to reduce carbon emissions from the operation of mobile networks. The widespread adoption of ultra-dense 5G and Internet of Things (IoT) networks will likely increase the energy demand from these networks significantly. In this paper, we model the density of the time required to charge BESS to its full capacity when the renewable energy sources can generate enough energy to meet the needs of the base station site and the density of the time required to completely deplete the energy stored in BESS when the renewable energy sources cannot generate a sufficient amount of energy to meet the demand of the site. We also investigate the influence of the design parameters, such as the energy supply-demand ratio, on the distribution of the time required to charge BESS to its full capacity (for a supply-demand ratio greater than one) and the time required to completely deplete the energy stored in BESS (for supply-demand ratio less than one).</pre>
  a978-99916-55-80-200430nas a2200145   4500000000100000000000100001008004100002260001200043100001600055700001600071700002400087245009400111856007200205490000700277       2024                            d  c09/20241 aMonika Nycz1 aTomasz Nycz1 aTadeusz Czachórski00aDropping or Marking: a Review and Evaluation of  Existing Fluid-Flow Approximation Models  uhttp://journals.pan.pl/Content/131546/PDF/BPASTS_2024_72_5_4300.pdf0 v7202260nas a2200205   4500000000100000000000100001008004100002260001500043100001900058700002400077700001700101700001600118700001800134700001800152700001900170700002000189245005500209490000700264520178300271       2024                            d  c07/11/20241 aGodlove Kuaban1 aTadeusz Czachórski1 aErol Gelenbe1 aPiotr Pecka1 aSapana Sharma1 aPradeep Singh1 aValery Nkemeni1 aPiotr Czekalski00aEnergy performance of self-powered Green IoT nodes0 v123 a<p>The widespread adoption of the Internet of Things (IoT) partly depends on the successful design and deployment of IoT nodes that can operate for several years without any service outage and the need to replace their energy storage systems (e.g., battery, capacitor, or supercapacitor) when all the stored energy is depleted or when the cycle life of the Energy Storage Systems (ESS) is reached. Replacing batteries in the case of large-scale IoT networks and nodes located in places that are hard to reach is very challenging and costly, requiring the design of IoT nodes that can operate for several years without the need for human intervention. One such example is the deployment of IoT nodes in large agricultural fields (for soil or crop monitoring) or a long-distance pipeline (for pipeline monitoring). This paper investigates the practical implications of imposing energy-saving thresholds on the energy performance metrics of green IoT nodes. We propose an energy packet-based model for the evaluation of the energy performance of a green IoT node with the possibility of switching the node to energy-saving regimes on the fly when the energy content of the ESS reaches defined thresholds. Configuring single or multiple thresholds improves the energy performance of the node significantly (e.g., increases the lifetime of the node and reduces the probability of service outage and energy wastage), and the value of the threshold(s) should be carefully chosen. The energy performance of the IoT node can also be improved by dimensioning the energy harvesting system to ensure that the node operates for several years without running out of energy (e.g., maximizing the lifetime of the nodes and minimising the probability of service outage and energy wastage).</p>
01664nas a2200181   4500000000100000000000100001008004100002260002500043100001900068700002400087700001700111700001600128700001900144700002000163245007400183856005100257520117400308       2024                            d  bIEEEaKrakow, Poland1 aGodlove Kuaban1 aTadeusz Czachórski1 aErol Gelenbe1 aPiotr Pecka1 aValery Nkemeni1 aPiotr Czekalski00aImpact of energy leakage on the energy performance of green IoT nodes  uhttps://ieeexplore.ieee.org/document/10786519 3 a<p>In the present paper, we investigate the impact of imperfections (non-idealities) of the energy storage system (e.g., batteries, capacitors, or supercapacitors) on the energy performance of green IoT nodes. In particular, we investigate the impact of energy leakage from the Energy Storage System (ESS) on important energy performance metrics, such as service outage probability, the density of the lifetime of the node, and the time-dependent mean number of energy packets (EPs) in the ESS. Also, we explore various strategies that can be employed to compensate for the impact of energy losses due to energy leakage on the energy performance metrics. Specifically, we examine two potential strategies for improving the energy performance of the IoT nodes: (i) through increasing the energy generation rate of the energy harvesters (e.g., by adding additional solar panels or replacing the existing solar panels with more efficient ones that can produce more energy), and (ii) through reducing the energy consumption rate of the IoT node (e.g., by configuring ESS energy thresholds below which the nodes are forced to operate in low energy consumption states).</p>
01697nas a2200157   4500000000100000000000100001008004100002260001300043100001900056700001900075700002400094700002000118700001800138245008300156520130000239                                       d  bSpringer1 aGodlove Kuaban1 aValery Nkemeni1 aTadeusz Czachórski1 aPiotr Czekalski1 aEric Djomadji00aDimensioning the renewable energy system of a Green Cellular Base Station Site3 a<p>The demand for mobile communication services has experienced astronomical growth in the past few decades and continues to grow steadily at a brisk rate. This is due partly to falling prices of user devices, the evolution of mobile applications, the widespread adoption of social media, and the recent advances in modern technologies such as the Internet of Things (IoT), cloud/fog computing, and Artificial Intelligence (AI). An expansion of mobile communication infrastructure to accommodate the rapidly growing demand and to migrate to new technologies (e.g., 4G/5G) have also resulted in a significant increase in the energy consumption of mobile networks. An attractive solution to reduce the carbon footprint of mobile networks and to reduce the energy bills of network operators is to deploy renewable energy systems(e.g., solar energy systems) to power base station sites. In this paper, we present a practical approach for dimensioning and evaluating renewable generation and storage systems for cellular base station sites. We use data collected from a base station site in Banda (North region of Cameroon) as input to the analytical model; we estimate the probability of service outage (when all the stored energy is consumed and the PV system is not able to generate energy).</p>
01866nas a2200169   4500000000100000000000100001008004100002260003900043100001900082700001600101700001600117700002400133700002000157245010800177520138900285020002201674       2025                            d  c07/2025bSpringer, ChamaSingapore1 aGodlove Kuaban1 aTomasz Nycz1 aMonika Nycz1 aTadeusz Czachórski1 aPiotr Czekalski00aModelling the Transient Evolution of Queues in Plugged-in Electric Vehicles(PEV) Fast Charging Stations3 a<p>The transportation sector is responsible for approximately 23% of global greenhouse gas (GHG) emissions, with road transportation contributing nearly 70% of these emissions. The widespread adoption of electric vehicles (EVs) is transforming this sector by reducing emissions and decreasing reliance on fossil fuels. However, the growing number of EVs presents significant challenges for charging infrastructure, particularly in managing long queues, extended wait times, and limited station capacity. Most existing studies on the performance of electric vehicle charging stations assume Poisson arrivals and exponential charging times, simplifications that often overlook real-world variability. This paper introduces a generalized queueing model that leverages empirical interarrival and charging duration data for more accurate performance evaluation. A transient analysis is conducted, examining two operational optimization strategies aimed at minimizing queue sizes during peak demand: (1) a queue management policy that encourages charging only up to a predefined state-of-charge (SoC) threshold instead of the typical 80–100%, and (2) dynamic control of the number of active charging ports based on demand. The results show that these operational optimization policies improve the efficiency of the charging station and significantly improve the customer experience.</p>
  a978-3-031-97635-302085nas a2200133   4500000000100000000000100001008004100002100001900043700002400062700001700086700002000103245013100123520169700254       2025                            d1 aGodlove Kuaban1 aTadeusz Czachórski1 aErol Gelenbe1 aPiotr Czekalski00aTime-Dependent Analysis of the Interaction Between Energy Harvesting, Leakage, and Consumption Processes in a Green IoT Device3 a<p>The Internet of Things (IoT) is transforming industries by enhancing productivity and efficiency; however, energy availability remains a significant challenge due to the limited capacity of batteries and supercapacitors powering IoT devices. The emergence of Green IoT (G-IoT) frameworks, which prioritize energy efficiency and renewable energy integration, offers a promising solution to address this challenge. Despite these advancements, energy storage systems (ESSs) face issues such as capacity degradation, leakage, and charge redistribution, which can lead to energy depletion and service disruptions. Traditional models often assume constant energy harvesting rates, overlooking the time-varying nature of environmental conditions that influence energy availability. In this paper, we propose a novel mathematical framework that incorporates time-dependent fluctuations in the energy harvesting rate to analyse the dynamic interactions between energy harvesting, leakage, and consumption in green IoT systems. Our approach includes an Energy Packet Network (EPN) model to represent transient energy dynamics and a Markov model to capture fluctuations in the energy harvesting rate. Through numerical simulations, we evaluate the impact of key design parameters, such as ESS capacity, mean energy consumption rate, energy leakage, and energy harvesting rate variations, on critical performance metrics including the probability of energy depletion and the transient mean number of stored energy packets. The results highlight the importance of considering time-varying energy harvesting in the design and optimization of IoT systems for long-term operation and sustainability.</p>
02041nas a2200157   4500000000100000000000100001008004100002260004100043100001900084700001700103700001800120700002400138700002000162245010800182520159300290       2025                            d  c02/12/2025bIEEEaMarrakech, Morocco1 aGodlove Kuaban1 aTulin Atmaca1 aZeynep Turgut1 aTadeusz Czachórski1 aPiotr Czekalski00aLeveraging Packet Aggregation to Optimise QoS and Energy Trade-offs in 5G/6G Optical Fronthaul Networks3 a<p>The growing demands of 5G and emerging 6G mobile networks—driven by the proliferation of IoT devices, immersive applications, and high-throughput services—necessitate more efficient and scalable fronthaul solutions. Open Radio Access Networks and cloud-RAN architectures offer promising frameworks by decoupling hardware and software and enabling centralised processing. However, the fronthaul segment still faces challenges related to bandwidth inefficiency, signalling overhead, and relatively high energy consumption, particularly due to the transmission of vast numbers of small packets. Packet aggregation emerges as a viable strategy to mitigate these challenges by grouping multiple small packets into larger payloads, thereby reducing protocol overhead, improving throughput efficiency, and lowering energy consumption in the network as a whole. This paper investigates the impact of packet aggregation on both quality of service (QoS) and energy efficiency in the fronthaul of 5G/6G mobile networks. While prior studies have examined its effects on throughput and latency, the implications for energy consumption remain underexplored. We evaluate how aggregation design parameters—such as time thresholds, size thresholds, and traffic intensity—influence performance metrics such as throughput and latency. We also propose ways to achieve an optimal trade-off between QoS and energy efficiency.&nbsp;Our findings offer insights into the design of energy-aware, delay-sensitive packet aggregation schemes tailored for next-generation mobile network architectures.</p>
02212nas a2200157   4500000000100000000000100001008004100002260003300043100001900076700002400095700001700119700001600136700002000152245011000172520177200282       2025                            d  c12/2025bIEEEaParis, France1 aGodlove Kuaban1 aTadeusz Czachórski1 aErol Gelenbe1 aPiotr Pecka1 aPiotr Czekalski00aEnergy Dynamics of Green IoT Nodes with Time-Varying Energy Harvesting, Leakage, and Consumption Patterns3 a<p>The growing proliferation of Internet of Things (IoT) devices has intensified the need for sustainable energy solutions, particularly in resource-constrained deployments where non-rechargeable batteries and supercapacitors are the primary energy sources. Green IoT (G-IoT) frameworks address this challenge by combining energy-saving techniques with energy harvesting from ambient sources such as solar power. However, the intermittent nature of renewable energy and the non-ideal behavior of energy storage systems—such as energy leakage and capacity degradation—complicate reliable energy provisioning. This paper presents a novel Markovian framework for modelling the coupled dynamics of time-varying solar energy harvesting, time-dependent energy consumption, and state-dependent energy leakage in G-IoT systems. Unlike traditional steady-state models, our approach uses Discrete-Time Markov Chains (DTMCs) to capture the stochastic variability in both energy harvesting and consumption processes. We also introduce a refined leakage model in which the leakage rate is dynamically dependent on the stored energy level, enabling a more realistic characterization of energy losses due to energy leakage. Through extensive analytical evaluation, we examine how key parameters—such as storage capacity, leakage rate coefficient, and energy harvesting and consumption patterns—affect critical performance metrics, including the mean stored energy and energy-related service outage probability. Furthermore, we propose a parameter tuning strategy to optimize energy reliability and storage efficiency. The proposed model provides valuable insights for the design and optimization of robust, energy-aware IoT systems powered by renewable energy sources.</p>
01648nas a2200145   4500000000100000000000100001008004100002260003300043100001900076700002400095700001700119700002000136245007700156520126900233       2025                            d  c12/2025bIEEEaParis, France1 aGodlove Kuaban1 aTadeusz Czachórski1 aTulin Atmaca1 aPiotr Czekalski00aAnalytical Power Efficiency Models for Open Radio Access Network (O-RAN)3 a<p>The rapid deployment of 5G and the anticipated evolution to 6G have intensified the need for energy-efficient radio access networks, driven by rapidly growing power demands, rising energy costs, and environmental concerns. Open Radio Access Networks (O-RAN) offer a flexible and scalable architecture; however, their disaggregated nature requires accurate power modelling to optimise energy consumption. This paper proposes enhanced power consumption models for O-RAN that account for power efficiency gains from resource sharing across multiple radio units (O-RUs) and the offloading of highly parallel baseband processing tasks to specialised energy-efficient hardware accelerators such as ASICs, FPGAs, or GPUs. The models are used to analyse the scaling behavior of O-RUs, distributed units (O-DUs), and centralized units (O-CUs) under varying deployment scenarios. Results show that O-RUs dominate the total network power budget, while O-DUs benefit from sublinear growth due to statistical multiplexing, and O-CUs scale the slowest owing to centralised control-plane sharing. Furthermore, the study identifies optimal ranges of RU-to-DU aggregation and accelerator utilization for maximising energy efficiency in large-scale 5G/6G O-RAN deployments.</p>
01617nas a2200145   4500000000100000000000100001008004100002260002900043100001900072700002400091700001700115700002000132245005800152520126100210                                       d  bIEEEaLas Vegas, NV, USA1 aGodlove Kuaban1 aTadeusz Czachórski1 aTulin Atmaca1 aPiotr Czekalski00aPower models for O-RAN O-DU planning and optimisation3 a<p>The transition from 5G to 6G networks is driving an unprecedented densification of Radio Access Networks (RANs), resulting in increased energy demand and operational costs. Open RAN (O-RAN) architectures, which disaggregate baseband processing across commercial off-the-shelf (COTS) servers, further amplify these challenges due to the power sensitivity of the Distributed Unit (O-DU) to traffic load variations.&nbsp;This paper develops a nonlinear O-DU power model that captures load-dependent efficiency effects and integrates it into a dynamic core management framework, which activates or deactivates CPU cores based on instantaneous traffic demand. The effectiveness of the proposed dynamic core management framework is investigated in multi-RU O-RAN scenarios, comparing the proposed dynamic approach with a static baseline. Simulation results show that the dynamic scheme reduces energy consumption by up to $59.42\%$ on average under light traffic, with instantaneous savings exceeding $70\%$ during off-peak periods, while maintaining performance. These findings demonstrate that workload-aware core provisioning can deliver substantial energy efficiency gains in O-RAN deployments, particularly in lightly to moderately loaded networks.</p>
01208nas a2200217   4500000000100000000000100001008004100002260003300043653000900076100002400085700002200109700001600131700001700147700001600164700001700180700001600197700001600213245007200229856005000301520063900351       2026                            d  c02/2026bIEEEaLas Vegas USA10aLoRa1 aTadeusz Czachórski1 aKrzysztof Grochla1 aPiotr Pecka1 aAnna Strzoda1 aJ. Fourneau1 aLynda Mokdad1 aMonika Nycz1 aTomasz Nycz00aThe impact of CSMA on LoRaWAN gateways' performance in IoT networks  uhttps://ieeexplore.ieee.org/document/113665363 a<p>The CSMA mode of operation was recently added to the LoRaWAN standard. We investigate, using a queueing model, how the introduction of CSMA in the communication between devices and the gate in LoRaWan may increase the probability of successful transmission. &nbsp;In the proposed model, customers (transmissions) are served by a multichannel station (gate), and the number of channels corresponds to the number of transmissions that can be performed in parallel. We introduce a queueing model with multiple attempts to find a free channel and investigate their impact on transmission throughput and reliability.<br />
&nbsp;</p>
00237nas a2200085   4500000000100000008004100001260001200042100002400054245007300078       2026                            d  c05/20261 aTadeusz Czachórski00a Queueing Theory and Diffusion Approximation with their Applications00529nas a2200181   4500000000100000008004100001260002600042653004100068100002400109700002200133700001600155700001700171700001800188700001700206700001600223700001600239245009200255       2026                            d  bIEEEaAvignon, France10aDiffusion approximation; LPWAN; CSMA1 aTadeusz Czachórski1 aKrzysztof Grochla1 aPiotr Pecka1 aAnna Strzoda1 aJean Fourneau1 aLynda Mokdad1 aMonika Nycz1 aTomasz Nycz00aThe Impact of CSMA on LoRaWAN Communication Efficiency: A Diffusion Approximation Model00847nas a2200181   4500000000100000000000100001008004100002260005000043100001900093700002100112700001400133700002200147700001400169700001300183245004800196300001200244520040900256       2014                            d  bSpringer International Publishing Switzerland1 aAdam Domański1 aJoanna Domańska1 aS. Chmiel1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aJavaScript Frameworks and Ajax Applications  a57–683 aJava Script frameworks are useful in the development of interactive web pages. Ajax technology supports a dialog with the WWW server. This article compares performance and functionality of the Ajax libraries for major Web browsers. The size of the libraries, their load time and execution time is compared. The article also evaluates the Document Object Model(DOM) support provided by selected libraries.00362nam a2200145   4500000000100000000000100001008004100002260005100043100002000094700001900114700002100133700001800154245002200172020002200194       2007                            d  bWyższa Szkoła Biznesu w Dąbrowie Górniczej1 aAndrzej Grzywak1 aAdam Domański1 aJoanna Domańska1 aM. Rostański00aSieci komputerowe  a978-83-88936-34-000322nas a2200121   4500000000100000000000100001008004100002100001600043700001900059700002100078245007700099022002400176       2010                            d1 aD. Augustyn1 aAdam Domański1 aJoanna Domańska00aA choice of optimal packet dropping function for active queue management  aISBN 3-642-13860-8.00377nam a2200109   4500000000100000000000100001008004100002260011800043100002100161245006300182020002200245       2014                            d  bInstytut Informatyki Teoretycznej i Stosowanej Polskiej Akademii NaukaPracownia Komputerowa Jacka Skalmierskiego1 aJoanna Domańska00aMarkowowskie modele natężenia przesyłów internetowych.  a978-83-62652-69-301416nas a2200181   4500000000100000000000100001000000100002008004100003100002100044700001900065700001600084700001700100245007400117300001400191490000700205520100800212022001401220       2014                            d1 aJoanna Domańska1 aAdam Domański1 aD. Augustyn1 aJerzy Klamka00aA red modified weighted moving average for soft real-time application  a697–7070 v243 aThe popularity of TCP/IP has resulted in an increase in usage of best-effort networks for real-time communication. Much effort has been spent to ensure quality of service for soft real-time traffic over IP networks. The Internet Engineering Task Force has proposed some architecture components, such as Active Queue Management (AQM). The paper investigates the influence of the weighted moving average on packet waiting time reduction for an AQM mechanism: the RED algorithm. The proposed method for computing the average queue length is based on a difference equation (a recursive equation). Depending on a particular optimality criterion, proper parameters of the modified weighted moving average funtion can be chosen. This change will allow reducing the number of violations of timing constraints and better use of this mechanism for soft real-time transmissions. The optimization problem is solved through simulation performed in OMNeT++ and later verified experimentally on a Linux implementation.  a1641-876X01274nas a2200157   4500000000100000000000100001000000100002008004100003100002100044700001600065700001900081245012300100490000700223520087200230022001401102       2012                            d1 aJoanna Domańska1 aD. Augustyn1 aAdam Domański00aThe choice of optimal 3-rd order polynomial packet dropping function for NLRED in the presence of self-similar traffic0 v603 aAlgorithms of queue management in IP routers determine which packet should be deleted when necessary. The article investigates the influence of the self-similarity on the optimal packet rejection probability function in a special case of NLRED queues. This paper describes another approach to the non-linear packet dropping function. We propose to use the solutions based on the polynomials with degree equals to 3. The process of obtaining the optimal dropping packets function has been presented. Our researches were carried out using the Discrete Event Simulator OMNET++. The AQM model was early verified using the discrete-time Markov chain. The obtained results show that the traffic characteristic has the great impact on the network node behavior, but self-similarity of network traffic has no influence on the choosing of the optimal dropping packet function.  a0239-752800357nas a2200145   4500000000100000000000100001008004100002260002000043100002100063700001900084700001700103700002100120700002300141245004700164       2008                            d  bWydawnictwo WSB1 aJoanna Domańska1 aAdam Domański1 aJerzy Klamka1 aJoanna Domańska1 aAdrian Kapczyński00aAdvanced problems of Internet technologies00924nas a2200181   4500000000100000000000100001008004100002260003200043100002100075700001900096700001600115700002200131700001400153700001300167245009500180300001200275520045500287       2011                            d  bSpringeraBerlin Heidelberg1 aJoanna Domańska1 aAdam Domański1 aD. Augustyn1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aThe Impact of the Modified Weighted Moving Average on the Performance of the RED Mechanism  a37–443 aAlgorithms of queue management in IP routers determine which packet should be deleted shen necessary. the article investigates the influence of weighted movind average on the performance of the RED mechanism. The proposed average queue length calculating method is based on a difference equation (a recursive equation). Depending on a particular criterion of optimization a proper parameters of modified weighted moving average function may be chosen00401nas a2200157   4500000000100000000000100001008004100002260004300043100002100086700001900107700001600126700001400142700001400156700001400170245005900184       2005                            d  bWydawnictwa Komunikacji i Łączności1 aJoanna Domańska1 aAdam Domański1 aP. Kasprzyk1 aM. Widera1 aA. Widera1 aPiotr Gaj00aSystemy czasu rzeczywistego Projektowanie i aplikacje.00479nas a2200157   4500000000100000000000100001008004100002260002000043100002100063700002300084700002500107700002000132245006300152490000700215520009900222       2009                            d  bSpringer Verlag1 aJoanna Domańska1 aPrzemysław Głomb1 aPrzemysław Kowalski1 aSławomir Nowak00aModeling of Internet 3D Traffic Using Hidden Markov Models0 v643 a<p>Artykuł podejmuje temat modelowania złożoności przesyłu siatek progresywnych 3D.</p>
00340nas a2200133   4500000000100000000000100001008004100002260002000043100001900063700002100082700002000103700002300123245006000146       2008                            d  bWydawnictwo WSB1 aAdam Domański1 aJoanna Domańska1 aAndrzej Grzywak1 aAdrian Kapczyński00aZastosowanie Internetu w społeczeństwie informacyjnym00913nas a2200169   4500000000100000000000100001008004100002260009000043100001900133700002100152700001700173700002000190700001800210245008300228520041000311020002200721       2013                            d  bAcademy of Business in Dabrowa GorniczaaCielplaka Str. 1c, 41-300 Dąbrowa Górnicza1 aAdam Domański1 aJoanna Domańska1 aJerzy Klamka1 aPiotr Pikiewicz1 aM. Rostański00aInternet in the Information Society Computer Systems Architecture and Security3 aIn this article we study a model of network transmissions with Active Queue Management in an intermadiate IP router. We use the OMNET++ discrete event simulator to model the different variants of the CHOKe algorithms. We model a system where CHOKe, xCHOKe and gCHOKe are the AQM policy. The obtained results show the behaviour of these algorithms and confirm their advantage over a standard RED algorithm.  a978-83-62897-40-700797nas a2200169   4500000000100000000000100001000000100002008004100003100001900044700002100063700001700084245005300101300001200154490000700166520044000173022001400613       2013                            d1 aAdam Domański1 aJoanna Domańska1 aJerzy Klamka00aAnalysis of CHOKe-Family Active Queue Management  a49–660 v253 aIn the article we study a model of network transmissions with Active Queue Management in an intermediate IP router. We use the OMNET++ discrete event simulator to model the varies variants of the CHOKe algorithms. We model a system where CHOKe, xCHOKe and gCHOKe are the AQM policy. The obtained results shows the behaviour of these algorithms. The paper presents also the implementation of AQM mechanisms in the router based on Linux.  a1896-533400611nas a2200253   4500000000100000000000100001000000100002008004100003100001500044700002300059700002700082700001900109700001400128700001400142700001500156700002100171700001700192700001600209700002000225245007500245300001400320490000900334022001400343       2013                            d1 aE. Helwich1 aJ. Wójkowska-Mach1 aM. Borszewska-Kornacka1 aJ. Gadzinowski1 aA. Kordek1 aD. Pawlik1 aJ. Szczapa1 aJoanna Domańska1 aJerzy Klamka1 aP.B. Heczko1 aE. Gułczyńska00aEpidemiologia zakażeń noworodków z bardzo małą masą urodzeniową  a224–2310 vXVII  a1428-345X00890nas a2200253   4500000000100000000000100001000000100002008004100003100002300044700001900067700002700086700002100113700001900134700001900153700001500172700001400187700001400201700001500215700001600230245016200246300001100408490000600419520021100425       2014                            d1 aJ. Wójkowska-Mach1 aA. Różańska1 aM. Borszewska-Kornacka1 aJoanna Domańska1 aJ. Gadzinowski1 aE. Gulczyńska1 aE. Helwich1 aA. Kordek1 aD. Pawlik1 aJ. Szczapa1 aP.B. Heczko00aNecrotising Enterocolitis in Preterm Infants: Epidemiology and Antibiotic Consumption in the Polish Neonatology Network Neonatal Intensive Care Units in 2009  ae928650 v93 aThe aim of this study was to describe the epidemiology of necrotising enterocolitis (NEC), antibiotic consumption and the usefulness of microbiological tests in very low birth weight (VLBW) Polish newborns.01496nas a2200265   4500000000100000000000100001008004100002100002300043700001900066700001900085700002700104700002100131700001500152700001500167700001400182700001400196700001900210700001500229700001400244245013900258300001400397490000700411520079800418022001401216       2014                            d1 aJ. Wójkowska-Mach1 aE. Gulczyńska1 aM. Nowiczewski1 aM. Borszewska-Kornacka1 aJoanna Domańska1 aA. Merritt1 aE. Helwich1 aA. Kordek1 aD. Pawlik1 aJ. Gadzinowski1 aJ. Szczapa1 aP Adamski00aLate-onset bloodstream infections of Very-Low-Birth-Weight infants: data from the Polish Neonatology Surveillance Network in 2009-2011  a339–3460 v143 aBackground: Late-Onset Bloodstream infections (LO-BSI) continue to be one of the most important complications associated with hospitalization of infants born with very low birth weight )VLBW). Ahe aims of this stydy were to assess the epidemiology of LO-BSI together with the risk factors and the distribution of causative pathogens at six Polish neonatal intensive care units that participated in the Polish Neonatology Surveillance Network from January 1, 2009 to December 31, 2011. Conclusions: Independent risk factor for LO-BSI in VLBV infants are: low gestational age and requirement for surgery. The incidence rates of LO-BSI especially CVC-BSI were higher in the Polish NICUs surveillance than those of other national networks, similar to the central- and peripheral utilization ratio.  a1471-233400650nas a2200265   4500000000100000000000100001000000100002008004100003260000600044100002300050700002700073700002100100700001900121700002000140700001500160700001400175700001400189700001500203700001700218700001400235245010000249300001400349490000700363022001400370       2012                            d  c71 aJ. Wójkowska-Mach1 aM. Borszewska-Kornacka1 aJoanna Domańska1 aJ. Gadzinowski1 aE. Gułczyńska1 aE. Helwich1 aA. Kordek1 aD. Pawlik1 aJ. Szczapa1 aJerzy Klamka1 aP. Heczko00aEarly-onset Infections of Very-low-birth-weight Infants in Polish Neonatal Intensive Care Units  a691–6950 v31  a0891-366800851nas a2200169   4500000000100000000000100001008004100002260003200043100001500075700001900090700002100109700002300130245005700153490000800210520044100218020002200659       2011                            d  bSpringeraBerlin Heidelberg1 aM. Hubczyk1 aAdam Domański1 aJoanna Domańska1 aAdrian Kapczyński00aInternet - Technical developments and Applications 20 v1183 aIn this paper we have examined Local and Remote File Inclusion vulnerabilities in details. It is proven that these security flaws may lead to variety of problems including generation of big load to the server as well as disclosure of files which should not be accessible by the clients. These attacks are not so popular as SQL injection or Cross-Site Scripting but still they are subject of serious threat because of their consequences.  a978-3-642-25354-600302nas a2200121   4500000000100000000000100001008004100002260004300043100002000086700002100106245004700127490000600174       2007                            d  bWydawnictwa Komunikacji i Łączności1 aSławomir Nowak1 aJoanna Domańska00aSieci Komputerowe Aplikacje i zastosowania0 v200308nas a2200121   4500000000100000000000100001008004100002100002000043700002100063700001900084245006900103022001400172       2009                            d1 aSławomir Nowak1 aJoanna Domańska1 aAdam Domański00aSimulation models of fair scheduling for the TCP and UDP streams  a1896-533400417nam a2200169   4500000000100000000000100001008004100002260000800043100002000051700002100071700002200092700002000114700002100134700002200155245004800177020002200225       2009                            d  bWSB1 aSławomir Nowak1 aJoanna Domańska1 aKrzysztof Grochla1 aSławomir Nowak1 aJoanna Domańska1 aKrzysztof Grochla00aSymulator zdarzeń dyskretnych OMNeT++v.3.3  a978-83-88936-70-801032nas a2200193   4500000000100000000000100001000000100002008004100003260000700044100002000051700002100071700001800092700002300110245008500133300001400218490000700232520058500239022001400824       2010                            d  c111 aSławomir Nowak1 aJoanna Domańska1 aMateusz Nowak1 aPrzemysław Głomb00aProgressive 3D meshes transmission: traffic generating and simulation evaluation  a247–2590 v223 aThe paper presents issues related to the progressive transmission of 3D meshes in clientserver architecture. A tool generating network traffic for different 3D scenes and scenarios is presented. The objective was to evaluate the transmission dynamics and possible impact of 3D traffic on the amount of data available in VR browsing client, which relates to browsing QoE (Quality of Experience). Simulation studies were carried out and HMM model of traffic generated by 3D server was worked out, planned for use in modeling works on future internet protocols, servicing 3D traffic.  a1896-533401091nas a2200193   4500000000100000000000100001008004100002260003200043100001400075700001900089700002100108700002200129700001400151700001300165245002200178490000800200520066700208020002200875       2012                            d  bSpringeraBerlin Heidelberg1 aP. Pawlas1 aAdam Domański1 aJoanna Domańska1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aComputer Networks0 v2913 aThis article describes the universal web pages content parser-cross-platform application enhancing the process of data extraction from the web pages. In this implementation user friendly interface, possibility of significant automation and reusability of already created patterns had been the key elements. Moreover, the original approach to the issue of parsing the not well-formed HTML, stating the application`s core, is precisely presented. Universal web pages content parser shows that the simplified web scrapping utility may be available to masses and not well-formed HTML sources may feed useful tree-like data structures as well as the well-formed ones.  a978-3-642-31216-802454nas a2200253   4500000000100000000000100001008004100002100002800043700002100071700003000092700002100122700002000143700002200163700001600185700002100201700001800222700001900240245010500259856007100364300001200435490000700447520173200454022001402186       2016                            d1 aJadwiga Wójkowska-Mach1 aT. Allen Merritt1 aMaria Borszewska-Kornacka1 aJoanna Domańska1 aEwa Gulczyńska1 aMarek Nowiczewski1 aEwa Helwich1 aAgnieszka Kordek1 aDorota Pawlik1 aPaweł Adamski00aDevice-associated pneumonia of very low birth weight infants in Polish Neonatal Intensive Care Units  uhttp://www.sciencedirect.com/science/article/pii/S1896112615000498  a90 - 950 v613 a<p>AbstractPurpose Late-Onset Pneumonia (LO-PNEU) is still the most important complication associated with the hospitalization of infants with very low birth weight (&lt;1501 g). The purpose of this paper is to summarize the results of an ongoing surveillance program defining LO-PNEU as associated or not associated with respiratory support in the \{NICU\} and distribution of causative pathogens from the Polish Neonatology Surveillance Network (PNSN). Materials and methods Surveillance of infections was conducted in the years 2009–2011 at six Polish NICUs. Results The incidence was 3.1/1000 \{NICU\} patient days (pds). The mean gestational age and birth weight among infants with LO-PNEU were significantly lower. The \{VAP\} incidence was of 18.2/1000 \{NICU\} pds for mechanically ventilated (MV) infants, while the rates for those receiving only \{CPAP\} were as low as 7.7/1000 \{NICU\} pds. \{MV\} significantly increased the risk of PNEU, but \{MV\} or \{CPAP\} for &lt;10 days did not increase the risk of LO-PNEU. Significantly associated with LO-PNEU was the use of central or peripheral venous catheters and total parenteral nutrition for longer periods. Microorganisms isolated in cases of LO-PNEU were Gram-positive cocci (53.5%) and Gram-negative rods, with predominating E. coli. Non fermentative bacilli were significantly more frequent in cases of \{VAP\} than in other cases. Conclusions Observed incidence rates associated with \{VAP\} and CPAP-PNEU, were higher than in other national surveillance systems and expressing the feasibility of lowering the risk of LO-PNEU and increasing patient safety. The incidence of pneumonia was found to be lower when using \{CPAP\} as compared to using MV.</p>
  a1896-112600706nas a2200217   4500000000100000000000100001008004100002100002100043700002300064700003100087700002800118700003100146700001700177700002800194700002100222700001900243700002100262245018800283300001000471490000700481       2018                            d1 aAnna Różańska1 aAndrzej Jarynowski1 aKatarzyna Kopeć-Godlewska1 aJadwiga Wójkowska-Mach1 aAgnieszka Misiewska-Kaczur1 aMarzena Lech1 aMałgorzata Rozwadowska1 aMarlena Karwacka1 aJoanna Liberda1 aJoanna Domańska00aDoes surgical site infection after Caesarean section in Polish hospitals reflect high-quality patient care or poor postdischarge surveillance? Results from a 3-year multicenter study.  a20-250 v4600408nas a2200145   4500000000100000000000100001008004100002260003500043100001800078700002000096700002100116245005400137856004900191020002200240       2019                            d  c06/2019bIEEEaPrague, Czechia1 aMateusz Nowak1 aSławomir Nowak1 aJoanna Domańska00aCognitive Routing for Improvement of IoT Security  uhttps://ieeexplore.ieee.org/document/8822037  a978-1-7281-3236-501711nas a2200181   4500000000100000000000100001008004100002260001400043100001700057700002100074700002000095700001800115700002000133245006300153856005800216490000800274520124700282       2020                            d  cJuly 20201 aErol Gelenbe1 aJoanna Domańska1 aPiotr Fröhlich1 aMateusz Nowak1 aSławomir Nowak00aSelf-Aware Networks that Optimize Security, QoS and Energy  uhttps://ieeexplore.ieee.org/abstract/document/91035250 v1083 aThe need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE), with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms which operate adaptively to attain the required performance and QoE. We survey the premises for these ideas coming from Cognitive Science and Active Networks, and review recent work on self-aware computer systems and networks, including those that propose the use of Software Defined Networks as a means to implement these concepts. Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding Quality of Service (QoS) and security are also presented. Some conclusions and suggestions for further work are also presented
00399nas a2200145   4500000000100000000000100001008004100002260003700043100001700080700002200097700002000119700002100139245004400160856004900204       2020                            d  c10/2020bIEEEXpressaNice France1 aErol Gelenbe1 aMiltiadis Siavvas1 aPaweł Boryszko1 aJoanna Domańska00aOptimum checkpoints for time and energy  uhttps://ieeexplore.ieee.org/document/928595900430nas a2200157   4500000000100000000000100001008004100002260000900043100002000052700001900072700002100091700001800112700001900130245011600149490000700265       2020                            d  c20201 aKatarzyna Filus1 aAdam Domański1 aJoanna Domańska1 aDariusz Marek1 aJakub Szyguła00aLong-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis0 v2200540nas a2200181   4500000000100000000000100001008004100002260003800043100002000081700002200101700002100123700001700144245008700161856006600248300001200314490001000326022002200336       2021                            d  bSpringer International Publishing1 aKatarzyna Filus1 aMiltiadis Siavvas1 aJoanna Domańska1 aErol Gelenbe00aThe Random Neural Network as a Bonding Model for Software Vulnerability Prediction  uhttps://link.springer.com/chapter/10.1007/978-3-030-68110-4_7  a102-1160 v12527  a978-3-030-68110-401179nas a2200181   4500000000100000000000100001008004100002260003800043100002000081700002100101700001700122245007000139856006600209300001000275490001000285520068000295022002200975       2021                            d  bSpringer International Publishing1 aKatarzyna Filus1 aJoanna Domańska1 aErol Gelenbe00aRandom Neural Network for Lightweight Attack Detection in the IoT  uhttps://link.springer.com/chapter/10.1007/978-3-030-68110-4_5  a79-910 v125273 a<p>Cyber-attack detection has become a basic component of all information processing systems, and once an attack is detected it may be possible to block or mitigate its effects. This paper addresses the use of a learning recurrent Random Neural Network (RNN) to build a lightweight detector for certain types of Botnet attacks on IoT systems. Its low computational cost based on a small 12-neuron recurrent architecture makes it particularly attractive for edge devices. The RNN can be trained off-line using a fast simplified gradient descent algorithm, and we show that it can lead to high detection rates of the order of 96%, with false alarm rates of a few percent.</p>
  a978-3-030-68110-400460nas a2200169   4500000000100000000000100001008004100002100002000043700002000063700002100083700002200104700001700126245007700143856004500220490001100265022001400276       2021                            d1 aKatarzyna Filus1 aPaweł Boryszko1 aJoanna Domańska1 aMiltiadis Siavvas1 aErol Gelenbe00aEfficient Feature Selection for Static Analysis Vulnerability Prediction  uhttps://www.mdpi.com/1424-8220/21/4/11330 v21 (4)  a1424-822000416nas a2200157   4500000000100000000000100001008004100002100002000043700002000063700001900083700002100102245006300123856004600186490001200232022001400244       2021                            d1 aŁukasz Sobczak1 aKatarzyna Filus1 aAdam Domański1 aJoanna Domańska00aLIDAR Point Cloud generation for SLAM algorithm evaluation  uhttps://www.mdpi.com/1424-8220/21/10/33130 v21 (10)  a1424-822000473nas a2200169   4500000000100000000000100001008004100002100001900043700001900062700002100081700001800102700002000120700001800140245008800158856004600246490001100292       2021                            d1 aJakub Szyguła1 aAdam Domański1 aJoanna Domańska1 aDariusz Marek1 aKatarzyna Filus1 aSzymon Mendla00aSupervised learning of Neural Networks for Active Queue Management in  the Internet  uhttps://www.mdpi.com/1424-8220/21/15/49790 v21(15)16164nas a2200253   45000000001000000000001000010080041000022600021000431000025000647000022000897000024001117000023001357000025001587000020001837000020002037000021002237000026002447000029002707000017002997000023003167000022003392450090003615201545900451       2022                            d  c03/2022aBelgium1 aCharalampos Marantos1 aMiltiadis Siavvas1 aDimitrios Tsoukalas1 aChristos Lamprakos1 aLazaros Papadopoulos1 aPaweł Boryszko1 aKatarzyna Filus1 aJoanna Domańska1 aApostolos Ampatzoglou1 aAlexander Chatzigeorgiou1 aErol Gelenbe1 aDionysios Kehagias1 aDimitrios Soudris00aSDK4ED: One-click platform for Energy-aware, Maintainable and Dependable Applications3 a<p><span style="font-family:sans-serif; font-size:14.944px">Developing</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">modern</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">secure</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">low-energy</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">applica</span><span style="font-family:sans-serif; font-size:14.944px">tions in a short time imposes new challenges and creates the need </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">designing</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">new</span><span style="font-family:sans-serif; font-size:14.944px">&nbsp;</span><span style="font-family:sans-serif; font-size:14.944px">software</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">tools</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">to</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">assist</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">developers</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">in</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">all</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">phases </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">application</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">development.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">The</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">design</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">such</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">tools</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">cannot </span><span style="font-family:sans-serif; font-size:14.944px">be</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">considered</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">a</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">trivial</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">task,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">as</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">they</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">should</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">be</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">able</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">to</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">provide</span> <span style="font-family:sans-serif; font-size:14.944px">optimization</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">multiple</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">quality</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">requirements.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">In</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">this</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">paper,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">we </span><span style="font-family:sans-serif; font-size:14.944px">introduce</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">SDK4ED</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">platform,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">which</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">incorporates</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">advanced</span> <span style="font-family:sans-serif; font-size:14.944px">methods and tools for measuring and optimizing maintainability, </span><span style="font-family:sans-serif; font-size:14.944px">dependability</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">energy.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">The</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">presented</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">solution</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">offers</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">a</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">com</span><span style="font-family:sans-serif; font-size:14.944px">plete</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">tool-flow</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">for</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">providing</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">indicators</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">optimization</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">meth</span><span style="font-family:sans-serif; font-size:14.944px">ods</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">with</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">emphasis</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">on</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">embedded</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">software.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">Effective</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">forecasting</span> <span style="font-family:sans-serif; font-size:14.944px">models</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">decision-making</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">solutions</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">are</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">also</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">implemented</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">to </span><span style="font-family:sans-serif; font-size:14.944px">improve</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">quality</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">software,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">respecting</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">constraints</span> <span style="font-family:sans-serif; font-size:14.944px">imposed</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">on</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">maintenance</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">standards,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">energy</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">consumption</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">limits </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">security</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">vulnerabilities.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">The</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">use</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">SDK4ED</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">platform</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">is</span> <span style="font-family:sans-serif; font-size:14.944px">demonstrated</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">in</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">a</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">healthcare</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">embedded</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">application.</span></p>
00451nas a2200145   4500000000100000000000100001008004100002100002000043700002000063700002100083700001500104245012400119856005500243490000700298       2022                            d1 aKatarzyna Filus1 aSławomir Nowak1 aJoanna Domańska1 aJakub Duda00aCost-Effective Filtering of Unreliable Proximity Detection Results Based on BLE RSSI and IMU Readings Using Smartphones  uhttps://www.nature.com/articles/s41598-022-06201-y0 v1200461nas a2200181   4500000000100000000000100001008004100002100001800043700001900061700001900080700002100099700002000120700002100140245005300161856004400214490000700258022001400265       2022                            d1 aDariusz Marek1 aJakub Szyguła1 aAdam Domański1 aJoanna Domańska1 aKatarzyna Filus1 aMarta Szczygieł00aAdaptive Hurst-Sensitive Active Queue Management  uhttps://www.mdpi.com/1099-4300/24/3/4180 v24  a1099-430000340nas a2200121   4500000000100000000000100001008004100002260003500043100002000078700002100098245004100119856005800160       2022                            d  bIEEEaPadova, 18-23 July, 20221 aKatarzyna Filus1 aJoanna Domańska00aNAM: What Does a Neural Network See?  uhttps://ieeexplore.ieee.org/abstract/document/989244200397nas a2200133   4500000000100000000000100001008004100002260001200043100002000055700002100075245007600096856008300172490000800255       2023                            d  c10/20221 aKatarzyna Filus1 aJoanna Domańska00aSoftware Vulnerabilities in TensorFlow-Based Deep Learning Applications  uhttps://www.sciencedirect.com/science/article/pii/S0167404822003406?via%3Dihub0 v12400454nas a2200145   4500000000100000000000100001008004100002100002000043700002000063700002100083700001900104245012300123856005500246490000700301       2022                            d1 aŁukasz Sobczak1 aKatarzyna Filus1 aJoanna Domańska1 aAdam Domański00aFinding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer  uhttps://www.nature.com/articles/s41598-022-22938-y0 v1200409nas a2200145   4500000000100000000000100001008004100002100002000043700002000063700002100083700001900104245008700123856004600210490000700256       2022                            d1 aŁukasz Sobczak1 aKatarzyna Filus1 aJoanna Domańska1 aAdam Domański00aBuilding a real-time testing platform for unmanned ground vehicles with UDP Bridge  uhttps://www.mdpi.com/1424-8220/22/21/84930 v2200460nas a2200157   4500000000100000000000100001008004100002260003800043100002000081700002000101700002100121700001900142700001700161245007400178856005000252       2022                            d  bIEEEaOsaka, 17-20 December, 20221 aKatarzyna Filus1 aŁukasz Sobczak1 aJoanna Domańska1 aAdam Domański1 aRafał Cupek00aReal-time testing of vision-based systems for AGVs with ArUco markers  uhttps://ieeexplore.ieee.org/document/1002081700353nas a2200121   4500000000100000000000100001008004100002100002000043700002100063245006700084856007200151490000800223       2023                            d1 aKatarzyna Filus1 aJoanna Domańska00aGlobal Entropy Pooling Layer for Convolutional Neural Networks  uhttps://www.sciencedirect.com/science/article/pii/S09252312230073850 v55500302nas a2200109   4500000000100000000000100001008004100002260003100043100002000074700002100094245007700115       2023                            d  bIEEEaThessaloniki, Greece1 aKatarzyna Filus1 aJoanna Domańska00aRecycling of Generic ImageNet-trained Models for Smart-city Applications01730nas a2200145   4500000000100000000000100001008004100002260003600043100001800079700002000097700002100117245010300138856005000241520129300291       2023                            d  c01/2024bIEEEaSorrento, Italy 1 aNur Kelesoglu1 aKatarzyna Filus1 aJoanna Domańska00aHierAct: a Hierarchical Model for Human Activity Recognition in Game-Like Educational Applications  uhttps://ieeexplore.ieee.org/document/103869193 a<p>Constantly evolving landscape of modern education makes the integration of technology crucial to enable more interactive and immersive learning experiences. One of the key problems in this domain is Human Activity Recognition (HAR), which uses standard smartphone sensors to understand and recognize user movements. While HAR exhibits high potential in this area, its persistent challenge is to differentiating between similar activities, whose occurrences can often lead to misclassification and reduced quality of applications. To tackle this problem, we employ specialized classifiers and use them in a hierarchical manner instead of using monolith multi-class models. We propose HierAct - a Hierarchical model that solves the multi-class human activity recognition problem. We also release a new HAR dataset - EduAct - (https://github.com/iitis/HierAct-Dataset) focused on activities that could be used to create game-like educational applications.Our results show that the performance of the proposed model is substantially better than other state-of-the-art machine learning models. Our work shows the potential of hierarchical classifiers in HAR applications for education, by offering educators and students a more accurate, reliable, and engaging interactive experience.</p>
00389nas a2200121   4500000000100000000000100001008004100002260007200043100001900115700002000134700002100154245009200175       2023                            d  c01/2024bIEEE International Conference on Big DataaSorrento, Italy1 aMarzena Halama1 aKatarzyna Filus1 aJoanna Domańska00aRobust category recognition based on deep templates for educational mobile applications00264nas a2200109   4500000000100000000000100001008004100002260002000043100002000063700002100083245005000104       2023                            d  aSorrento, Italy1 aKatarzyna Filus1 aJoanna Domańska00aNetSat: Network Saturation Adversarial Attack00387nas a2200133   4500000000100000000000100001008004100002260003500043100002000078700002000098700001900118700002100137245009500158       2023                            d  c01/2024bIEEEaSorrento, Italy1 aŁukasz Sobczak1 aKatarzyna Filus1 aMarzena Halama1 aJoanna Domańska00aVisual examination of relations between known classes for deep neural network classifiers.00838nas a2200289   4500000000100000000000100001008004100002100002200043700002400065700002500089700002500114700002300139700001700162700002100179700002100200700002600221700002000247700002100267700001900288700002600307700002200333700002900355700001700384700002300401700002300424245010100447       2024                            d1 aMiltiadis Siavvas1 aDimitrios Tsoukalas1 aCharalambos Marantos1 aLazaros Papadopoulos1 aChristos Lamprakos1 aOliviu Matei1 aChristos Strydis1 aMuhammad Siddiqi1 aPhilippe Chrobocinski1 aKatarzyna Filus1 aJoanna Domańska1 aParis Avgeriou1 aApostolos Ampatzoglou1 aDimitrios Soudris1 aAlexander Chatzigeorgiou1 aErol Gelenbe1 aDionysios Kehagias1 aDimitrios Tzovaras00aSDK4ED: a platform for building energy efficient, dependable, and maintainable embedded software00393nas a2200133   4500000000100000000000100001008004100002260001200043100002000055700002100075245008300096856007200179490000800251       2024                            d  c12/20241 aKatarzyna Filus1 aJoanna Domańska00aExtracting coarse-grained classifiers from large Convolutional Neural Networks  uhttps://www.sciencedirect.com/science/article/pii/S09521976240153550 v13800328nas a2200109   4500000000100000000000100001008004100002100002000043700002100063700001700084245011700101       2024                            d1 aKatarzyna Filus1 aJoanna Domańska1 aJerzy Klamka00aEvaluating Convolutional Neural Networks via measuring similarity of their first-layer filters and Gabor filters00371nas a2200133   4500000000100000000000100001008004100002260001200043100002000055700002100075245006100096856007200157490000800229       2024                            d  c12/20241 aKatarzyna Filus1 aJoanna Domańska00aSimilarity-driven adversarial testing of neural networks  uhttps://www.sciencedirect.com/science/article/pii/S09507051240125530 v30500597nas a2200181   4500000000100000000000100001008004100002260001200043100001800055700002000073700002000093700002100113700002200134700001800156700003000174245008000204856013100284       2025                            d  c02/20251 aNur Kelesoglu1 aŁukasz Sobczak1 aPiotr Biernacki1 aJoanna Domańska1 aFrancisco Ferrão1 aShahid Mumtaz1 aFideline Tchuenbou-Magaia00aArtificial Intelligence for Electronic-based Systems in Safety Applications  uhttps://www.researchgate.net/publication/389204536_Artificial_Intelligence_for_Electronic-based_Systems_in_Safety_Applications00323nas a2200121   4500000000100000000000100001008004100002100002000043700002100063245009500084490000800179022001400187       2025                            d1 aKatarzyna Filus1 aJoanna Domańska00aWhat is the doggest dog? Examination of typicality perception in ImageNet-trained networks0 v188  a0893-608001474nas a2200157   4500000000100000000000100001008004100002260000800043100002000051700002000071700002100091245008000112856004400192300001400236520106600250       2025                            d  bACM1 aŁukasz Sobczak1 aPiotr Biernacki1 aJoanna Domańska00aVisual Encoding Method for Semantic Mapping with Federated Learning Concept  uhttps://doi.org/10.1145/3704413.3765513  a428 - 4353 a<p>We present a visual encoding method for semantic mapping in indoor environments, designed to minimize redundancy in image data and support federated learning across a fleet of service robots. Our pipeline combines 2D LiDAR-based segmentation with RGB image filtering based on geometric orientation, distance, visibility, and uniqueness. The result is a compact set of representative visual samples suitable for downstream semantic tasks such as object recognition or language grounding. We evaluate our method in a Gazebo simulation using a TurtleBot platform and compare it against a naive odometry-based sampling strategy. Our approach achieves up to 57.5\% reduction in collected images while preserving scene coverage. Additionally, we demonstrate how multiple robots can collaboratively improve the visual map in a federated setup, reducing collection time and enabling model generalization across diverse environments. The proposed method offers an efficient and scalable solution for semantic mapping under bandwidth and computation constraints.</p>
00323nas a2200109   4500000000100000000000100001008004100002100002000043700002100063245006300084856006600147       2025                            d1 aKatarzyna Filus1 aJoanna Domańska00aHow CNNs and ViTs perceive similarities between categories  uhttps://link.springer.com/chapter/10.1007/978-3-032-06078-5_201702nas a2200133   4500000000100000000000100001008004100002260004600043100001900089700001800108700002100126245011700147520130400264       2025                            d  c2025bIEEE BigData 2025aMacau SAR, China1 aMarzena Halama1 aKonrad Połys1 aJoanna Domańska00aSpeech-based analysis of aggression in social content: resistance of Large Language Models to signal degradation3 a<p>Artificial Intelligence (AI) systems, including robotic assistants and voice interfaces, are increasingly present in public spaces such as airports, train stations, hospitals, and government offices, offering new opportunities to enhance user safety and comfort. Detecting aggression and hate speech in audio data from such locations is critical to protecting users and creating safe spaces. Previous approaches in this area have mainly relied on classification using classical Machine Learning (ML) methods and Deep Learning (DL) techniques, primarily focused on pure text analysis. However, most systems do not take into account real-world challenges such as background noise, incomplete utterances, or transcription errors resulting from automatic speech recognition. In this work, we propose a comprehensive approach that exploits the potential of Large Language Models (LLMs) to classify aggressive content in degraded audio transcriptions. We create a realistic test environment by converting tagged social media posts with offensive content into recordings with simulated noise and compare the performance of the models under different data quality conditions. Our research provides practical guidance on how to build more resilient and accessible voice moderation systems using LLM.</p>
01037nas a2200133   4500000000100000000000100001008004100002100001900043700002100062700002000083700002700103245005100130520072200181       2025                            d1 aMarzena Halama1 aJoanna Domańska1 aAnna Gancarczyk1 aMirzokhid Abdirakhimov00aTworzenie bazy danych zeolitów z użyciem LLM3 a<p>Praca dotyczy opracowania zautomatyzowanego systemu tworzenia i zarządzania bazą danych zeolitów z wykorzystaniem dużych modeli językowych (LLM). Zaprojektowane rozwiązanie umożliwia ekstrakcję, ujednolicanie i strukturyzację informacji dotyczących syntezy, właściwości oraz zastosowań zeolitów na podstawie dokumentów tekstowych, takich jak publikacje naukowe i raporty badawcze. W procesie budowy bazy wykorzystano dane International Zeolite Association – Structure Commission oraz model LLM do identyfikacji kluczowych parametrów syntezy. Opracowana baza danych stanowi fundament pod przyszły system rekomendacyjny wspierający dobór odpowiednich zeolitów do konkretnych zastosowań.</p>
02683nas a2200133   4500000000100000000000100001008004100002100001800043700002000061700002100081245010200102856005500204520229000259       2026                            d1 aNur Kelesoglu1 aŁukasz Sobczak1 aJoanna Domańska00aWhen LLMs Speak ZigBee: Exploring Low-Latency and Reasoning Models for Network Traffic Generation  uhttps://www.nature.com/articles/s41598-026-37246-y3 a<p>The generation of realistic network traffic is a critical requirement for testing, simulation, and security evaluation in ZigBee-based IoT systems. In this study, we propose a novel framework that extracts sample ZigBee traffic packets and generates semantically meaningful and protocol-compliant synthetic traffic by using Large Language Models (LLMs) such as GPT-4.1 and GPT-5. Unlike traditional rule-based or statistical generators, our approach is data-driven and incorporates sample-based few-shot learning, prompt engineering, and a human-in-the-loop feedback mechanism. To evaluate the effectiveness of the proposed framework, we conduct two sets of experiments. The first focuses on generating unidirectional traffic to emulate typical device-to-hub communication, while the second extends this setup to bidirectional exchanges, capturing realistic request--response dynamics and interaction patterns. The realism of the generated traffic is assessed using a multi-dimensional evaluation framework that includes statistical similarity measures, such as Jensen--Shannon Divergence, as well as protocol compliance, semantic correctness, temporal consistency, and diversity metrics. In addition, we compare the performance of LLM-based generators with classical deep learning baselines, including recurrent neural networks (RNNs) and generative adversarial networks (GANs). We further analyze the computational cost and the impact of internal reasoning effort on traffic generation by systematically evaluating different GPT-5 reasoning configurations. Experimental results show that both GPT-4.1 and GPT-5 successfully learn the temporal and structural dependencies of ZigBee traffic and significantly outperform RNN and GAN baselines in terms of semantic correctness and long-duration generation. Across all experiments, GPT-4.1 consistently generates traffic that more closely resembles real ZigBee traffic while requiring substantially lower computational resources. These findings highlight that low-latency, non-reasoning LLMs can be particularly well suited for highly structured, protocol-constrained network traffic generation tasks, and demonstrate the potential of LLM-based approaches for realistic IoT traffic generation in research and security evaluation.</p>
00560nas a2200109   4500000000100000008004100001260026300042100001800305700002000323700002100343245008600364       2026                            d  b(This work was partly supported by the National Centre for Research and Development (NCBR) and co-funded by the European Union under the European Funds for ModernEconomy(FENG)Programme(SMARTKonsorcja),projectno.FENG.01.01-IP.01-A0GV/24-00,)aKraków, Poland 1 aNur Kelesoglu1 aŁukasz Sobczak1 aJoanna Domańska00aCPU Utilization Forecasting in Large-Scale Cloud Clusters Using Gradient Boosting01663nas a2200121   4500000000100000008004100001260007000042100001900112700001800131700002100149245008600170520128500256       2026                            d  b2nd International Conference PUT STEM Day 2026: Book of Abstracts1 aMarzena Halama1 aKonrad Połys1 aJoanna Domańska00aAI-Assisted Zeolite Design through Retrieval-Augmented Scientific Language Models3 a<p style="border-style:none;line-height:normal;margin-bottom:.0001pt;text-align:justify;"><span style="color:black;font-size:11.0pt;font-weight:normal;" lang="EN-US">Designing materials such as zeolites is a major challenge due to the wide range of possible structures and synthesis conditions. Furthermore, knowledge on this subject is widely scattered across numerous publications and experimental notes. Therefore, large language models (LLMs) represent a promising tool for building knowledge bases, supporting the analysis of scientific literature, although their responses may be incomplete or fallible. In addition, in the case of zeolites, the relationship between material properties and their topology is of crucial importance, which limits the effectiveness of approaches based solely on text. In this study, we investigate a Retrieval-Augmented Generation (RAG) approach extended with a multimodal component that combines textual and structural information.</span></p><p style="border-style:none;line-height:normal;margin-bottom:.0001pt;text-align:justify;">&nbsp;</p><p style="border-style:none;line-height:normal;margin-bottom:.0001pt;text-align:justify;">&nbsp;</p><p style="border-style:none;line-height:normal;margin-bottom:.0001pt;text-align:justify;">&nbsp;</p>00343nas a2200121   4500000000100000008004100001260002500042100001900067700001800086700002000104700002100124245007600145       2026                            d  bAGH University Press1 aMarzena Halama1 aKonrad Połys1 aSławomir Nowak1 aJoanna Domańska00aExternal Semantic Memory as a Cognitive Layer for Large Language Models01600nas a2200133   4500000000100000008004100001260004000042100002000082700001800102700002100120245007800141856005000219520119700269       2026                            d  c10.08.2026bIEEEaKitakyushu, Japan1 aŁukasz Sobczak1 aNur Kelesoglu1 aJoanna Domańska00aReliability-Aware LLM Reasoning: Handling Uncertainty in Robot Perception  uhttps://ieeexplore.ieee.org/document/117148513 a<p>Robots operating in human environments must often make decisions based on perceptual information that<br>is uncertain, incomplete, or ambiguous. This paper proposes a reliability-aware reasoning framework that enables large<br>language models (LLMs) to account for perceptual uncertainty when selecting actions in human-robot interaction scenarios.<br>The environment is represented as a structured scene composed of detected objects enriched with confidence estimates, attribute reliability, and spatial uncertainty information. Using this representation, the LLM evaluates candidate objects through a reliability scoring mechanism that integrates multiple sources of perceptual evidence and supports uncertainty-aware decision making.The proposed approach is evaluated using perception episodes with controlled levels of uncertainty and compared with a baseline LLM-based matching strategy that ignores perceptual<br>reliability. Experimental results show that incorporating uncertainty-aware reasoning substantially improves decision<br>robustness under medium and high uncertainty conditions while reducing safety-critical behaviors caused by overconfident<br>autonomous decisions.</p>01381nas a2200133   4500000000100000008004100001260004000042100001800082700002100100700002000121245009900141856005000240520095700290       2026                            d  c10.08.2026bIEEEaKitakyushu, Japan1 aNur Kelesoglu1 aJoanna Domańska1 aŁukasz Sobczak00aRisk-Aware Response Refinement for Safer LLM-Powered Socially Assistive Robots in Elderly Care  uhttps://ieeexplore.ieee.org/document/117147883 a<p>Safe and reliable human–robot interaction remains a challenge, especially for Large Language Model (LLM)-powered socially assistive robots in elderly care. This paper proposes a risk-aware response framework that explicitly models the safety implications of user queries. &nbsp;We introduce a Query Risk Assessment Module (QRAM), which computes a Query Risk Score (QRS) using structured semantic indicators to classify user inputs into different risk levels. Based on this, we develop two strategies: risk-aware response refinement and risk-aware response generation. To assess safety, we introduce the Risk-Aware Response Safety Score (RRSS), a metric that captures both the presence of risk-bearing language and the absence of necessary safety-critical guidance. Experimental results across multiple LLMs demonstrate that incorporating query-level risk awareness reduces response-level risk, with both strategies outperforming raw responses.</p>00420nas a2200121   4500000000100000008004100001260003000042100001800072700002000090700002100110245011600131856005100247       2026                            d  c10/05/2026aNaples, Italy1 aNur Kelesoglu1 aŁukasz Sobczak1 aJoanna Domańska00aFrom Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interaction  uhttps://dl.acm.org/doi/10.1145/3776591.383386500536nas a2200157   4500000000100000008004100001100002000042700002200062700002000084700002300104700002000127700002100147700002100168245012200189856006700311       2026                            d1 aKatarzyna Filus1 aJorge Cruz-Duarte1 aMateusz Korpyś1 aAndrzej Kołodziej1 aAnna Gancarczyk1 aEl-Ghazali Talbi1 aJoanna Domańska00aFeature- and Similarity-Based Explainability of Experimental Materials Data via Black-Box Hyperparameter Optimization  uhttps://link.springer.com/chapter/10.1007/978-3-032-36226-1_1100338nas a2200109   4500000000100000008004100001260001600042100002000058700001800078700002100096245011100117       2026                            d  aBari, Italy1 aŁukasz Sobczak1 aNur Kelesoglu1 aJoanna Domańska00aKnowledge-Graph-Grounded Multimodal Explanations for Trustworthy Time-Series Forecasting in DevOps Systems00381nas a2200121   4500000000100000008004100001100002000042700002000062700001800082700002100100700003000121245010800151       2026                            d1 aKatarzyna Filus1 aObinna Nwadiuto1 aZlatka Stoeva1 aJoanna Domańska1 aFideline Tchuenbou-Magaia00aLightweight Time-Series Representation for Battery State of Health Estimation from Graphene Sensor Data00338nas a2200097   4500000000100000008004100001100001800042700002000060700002100080245013900101       2026                            d1 aNur Kelesoglu1 aŁukasz Sobczak1 aJoanna Domańska00aGrounding LLM-Generated Network Traffic in Semantics: A Knowledge-Graph Framework for Detecting and Explaining Protocol Hallucinations00610nas a2200253   4500000000100000000000100001008004100002260003800043100001400081700001900095700001500114700001600129700001800145700001400163700001700177700002000194700001600214700001700230700001300247700001700260700002400277700001500301245004000316       2012                            d  bPolitechnika WarszawskaaWarszawa1 aA. Bęben1 aJ. Śliwiński1 aP. Krawiec1 aPiotr Pecka1 aMateusz Nowak1 aB. Belter1 aJ. Gutkowski1 aŁ. Łopatowski1 aP. Białoń1 aJ.M. Batalla1 aM. Drwal1 aP. Rygielski1 aWojciech Burakowski1 aP. Krawiec00aInżynieria Internetu Przyszłości01551nas a2200169   4500000000100000000000100001008004100002260005000043100001700093700001700110700001600127700001700143700002500160245012000185300001400305520106200319       2014                            d  bSpringer International Publishing Switzerland1 aB. Wieczorek1 aM. Połomski1 aPiotr Pecka1 aS. Deorowicz1 aStanisław Kozielski00aAn Effective Way of Storing and Accessing Very Large Transition Matrices Using Multi-core CPU and GPU Architectures  a323–3343 aThe topic of probabilistic model checking algoritms of Markov processes makes the problem of storing and processing of very large transition matrices crucial. While the symbolic approach utilises very effective storage and access methods, like Multi-terminal Binary Decision Diagrams, the traditional explicit method of representing and solving the model using sparse matrices has many advantages, especially when we concired the flexibility of computing of different properties. We show and examine a compact representation of a very large transition matrix in the sparse form. The technique employs an effective method of compression, storage, and accessing of the matrix and is suitable for use in mutli-core CPU and GPU environments. Such large transition matrices consume a lot of space and cannot be considered as typical types of data for storage in database systems. Nevertheless, their storage and efficient access to them could be beneficial for solving of Markov models, so constructing specialised compression methods is obviously the way to go.00249nas a2200097   4500000000100000000000100001008004100002100002200043700001600065245007000081       2006                            d1 aKrzysztof Grochla1 aPiotr Pecka00aThe Markov chain model of RED active queuing management algorithm00269nas a2200097   4500000000100000000000100001008004100002100002200043700001600065245009000081       2006                            d1 aKrzysztof Grochla1 aPiotr Pecka00aAnaliza wydajności mechanizmu RED za pomocą łańcuchów Markowa z czasem ciągłym00337nas a2200145   4500000000100000000000100001008004100002260001300043100001800056700002000074700001600094700002200110245004500132300001400177       2011                            d  bSpringer1 aMateusz Nowak1 aSławomir Nowak1 aPiotr Pecka1 aKrzysztof Grochla00aContent Identification in PI CAN Network  a164–17201205nas a2200133   4500000000100000000000100001008004100002260003300043100001800076700001600094245009200110490000700202520086200209       2009                            d  bSpringer Berlin / Heidelberg1 aMateusz Nowak1 aPiotr Pecka00aSimulation and Analytical Evaluation of a Slotted Ring Optical Packet Switching Network0 v643 a<p>Optical Packet Switching (OPS) is a technology which thanks to its speed of operation may become popular in next-generation networks. Because optical packets are not buffered it is necessary to develop new mechanisms which would allow quality of service management in OPS networks. The paper discusses the architecture of OPS networks of the ring type with nodes enabling optical packet switching, equipped with the mechanism of Quality of Service (QoS) management. The differentiation of the quality of service was achieved by means of the proposed mechanism of priority registers with the timeout mechanism. This enables the network to be used for transporting various classes of data with varying requirements as to the quality of service. Simulation trials have been conducted and an analytical model for this type of network has been proposed.</p>
01165nas a2200181   4500000000100000000000100001008004100002260003600043100001800079700001600097700001600113700001700129700001900146245015900165490000900324520062800333020002200961       2010                            d  c8bSpringer Berlin / Heidelberg1 aMateusz Nowak1 aPiotr Pecka1 aS. Balandin1 aR. Dunaytsev1 aY. Koucheryavy00aSmart Spaces and Next Generation Wired/Wireless Networking. Third Conference on Smart Spaces, ruSMART 2010, and 10th International Conference, NEW2AN 20100 v62943 aModelling with use of Markov chains is difficult from computational point of view, because of a rapid increase in the number of states occurring even when there is only a slight increase in the model’s scale. However, thanks to the appropriate description of the model the number of states can be significantly reduced. This paper discusses a synchronous slotted-ring network with optical packet switching modelled by means of markovian chains with continuous time. Simplifying the description has made it possible to significantly reduce the model’s dimension without significantly diminishing the results’ accuracy.  a978-3-642-14890-301031nas a2200157   4500000000100000000000100001000000100002008004100003100001800044700001600062245007400078300001400152490000700166520068400173022001600857       2009                            d1 aMateusz Nowak1 aPiotr Pecka00aPriority Registers Mechanism for fair access to ring-type OPS network  a181–1910 v213 aThe paper discusses the architecture of ring-type OPS (Optical Packet Switching) networks with QoS management. The electronic-optical nodes of the network enable optical packet switching within the ring and electronic buffered interface for a client. The differentiation of the quality of service was achieved by means of the system of priorities, and thanks to proposed mechanism of priority registers fair access to the link was achieved. This enables the network to be used for transporting various classes of data with varying requirements as to the quality of service. Simulation trials have been conducted and an analytical model for this type of network has been proposed.  a1896–533401161nas a2200157   4500000000100000000000100001000000100002008004100003100002000044700001600064700001800080245006700098300001600165520080800181022001400989       2012                            d1 aSławomir Nowak1 aPiotr Pecka1 aMateusz Nowak00aRozproszona baza danych dla lokalizacji treści w sieci PI CAN  a1481–14903 aIdentyfikacja i lokalizacja treści stanowią podstawowe zagadnienia przy opracowywaniu sieci ukierunkowanych na treść (content-centric networks). W artykule przedstawiono implementację oraz badania wydajności mechanizmu Content Registry (CR), który został opracowany na potrzeby projektu Inżynieria Internetu Przyszłości, w części dotyczącej mechanizmów i wymagań dla sieci PI CAN (Paralel Internet Content Aware Network). CR umożliwia wydajne wyszukiwanie, w rozproszonej sieciowej bazie danych rekordów opisu treści, które zawierają podstawowe informacje o lokalizacji treści I wymaganiach w zakresie transmisji sieciowej. Rezultaty badań przedstawione w artykule potwierdzają wysoką efektywność proponowanego rozwiązania w różnych konfiguracjach i topologiach sieciowych.  a1230-349600953nas a2200133   4500000000100000000000100001008004100002260001600043100002000059700001600079700001800095245009500113520061100208       2012                            d  bIEEE Xplore1 aSławomir Nowak1 aPiotr Pecka1 aMateusz Nowak00aContent Registry - the distributed database of content localization for the PI CAN network3 aContent addressing and localization are basic issues in the structure of content-centric networks. This paper presents the implementation and performance evaluation of the Content Registry (CR), developed within the framework of the FIE (Future Internet Engineering) project to meet the requirements of the PI CAN network. The CR allows to find the desired Content Record (containing basic transmission parameters and localization of a content) in the network-distributed content database. The results confirm the CR is highly efficient in various configurations of the logical topology of connected nodes.00235nas a2200097   4500000000100000000000100001008004100002260000700043100001600050245007100066       2005                            d  c111 aPiotr Pecka00aSoftware library for the Markov analysis of complex queuing models00860nas a2200193   4500000000100000000000100001008004100002260003400043100001600077700001800093700002000111700002000131700002400151700001500175245006300190490000900253520038200262020002200644       2011                            d  bSpringeraBerlin / Heidelberg1 aPiotr Pecka1 aMateusz Nowak1 aSławomir Nowak1 aSergey Balandin1 aKoucheryavy Yevgeni1 aHu Honglin00aSmart Spaces and Next Generation Wired/Wireless Networking0 v68693 aContent addressing and localization are the basic issues in design of content-aware networks (CAN). The paper summarizes assumptions for content-aware networks and presents content localization algorithm for PI-CAN, developed within FIE (Future Internet Engineering) project. The algorithm allows to find location of desired content in distributed content database in time O(1)  a978-3-642-22874-200390nas a2200157   4500000000100000000000100001000000100002008004100003100001600044700001800060700001600078245010300094300001400197490000700211022001400218       2011                            d1 aArtur Rataj1 aMateusz Nowak1 aPiotr Pecka00aModelling CTMC with a standard programming language and using conventions from computer networking  a229–2430 v23  a1896-533401417nas a2200145   4500000000100000000000100001008004100002100002000043700001800063700002200081700001600103245011500119300001200234520102500246       2016                            d1 aSławomir Nowak1 aMateusz Nowak1 aKrzysztof Grochla1 aPiotr Pecka00aEfektywne rozgłaszanie w wielohopowej sieci bezprzewodowej. Effective Broadcasts in Multihop Wireless Network  a887-8913 a<p>Znanych jest wiele rozwiązań problemu rozgłaszania wiadomości w sieciach bezprzewodowych typu ad-hoc. Rozwiązania te różnią się w zależności od wielkości sieci, dostępności informacji o topologii, modelu komunikacji, wymagań gwarancji dostarczenia wiadomości itp. Sieci dedykowane są do różnych zastosowań i różnią się specyfiką występujących w nich problemów związanych z komunikacją. W artykule przedstawiono kilka znanych rozwiązań problemu rozgłaszania oraz przedstawiono autorskie rozwiązanie, dedykowane do specyficznego problemu sieci typu AMI (Advanced Metering Infrastructure - zaawansowana infrastruktura pomiarowa). Sieć ta zakładała model komunikacji jeden-do-wielu, stosunkowo niewielką dynamikę zmian w sieci, możliwość istnienia długich ścieżek na drodze transmisji oraz szereg innych wymagań. Analizowane algorytmy nie spełniały postawionych wymagań. Artykuł opisuje opracowaną motodę i porównuje ją do wybranych rozwiązań referencyjncych.</p>
01158nas a2200229   4500000000100000000000100001000000100002008004100003260004400044100002000088700001800108700002200126700001600148700001400164700002200178700002000200245007300220856004800293300001000341520055500351020002200906       2017                            d  bSpringer International PublishingaCham1 aSławomir Nowak1 aMateusz Nowak1 aKrzysztof Grochla1 aPiotr Pecka1 aPiotr Gaj1 aAndrzej Kwiecień1 aMichał Sawicki00aApplication of Fault-Tolerant GQP Algorithm in Multihop AMI Networks  uhttps://doi.org/10.1007/978-3-319-59767-6_6  a70-803 a<p>The paper presents the evaluation of the GQP algorithm in AMI networks using real map based network topologies (low, medium and high density of buildings). The algorithm was compared to the reference algorithms: CB, SBA and DP. It was shown that the GQP algorithm presents the good scalability, relatively optimal number of selected forwarders and by introducing the redundancy factor, good fault tolerance. An improvement to GQP algorithm to add a redundancy factor is proposed, what allows to execute fault tolerant broadcast transmission.</p>
  a978-3-319-59767-600667nas a2200217   4500000000100000000000100001008004100002260001300043100002200056700001800078700001600096700002000112700001400132700001400146700001700160245010900177856006900286300001200355490006000367020002200427       2017                            d  bSpringer1 aKrzysztof Grochla1 aMateusz Nowak1 aPiotr Pecka1 aSławomir Nowak1 aA Zgrzywa1 aK Choroś1 aA Siemiński00aInfluence of message-oriented middleware on performance of network management system: a modelling study.  uhttps://link.springer.com/chapter/10.1007%2F978-3-319-43982-2_33  a379-3930 vAdvances in Intelligent Systems and Computing, Vol. 506  a978-3-319-43981-500333nas a2200133   4500000000100000000000100001008004100002260000900043100001600052700001800068700001600086700002000102245007700122       2018                            d  c20181 aPiotr Pecka1 aMateusz Nowak1 aArtur Rataj1 aSławomir Nowak00aSolving large Markov models described with standard programming language00328nas a2200133   4500000000100000000000100001008004100002260001200043100001600055700001800071245005300089856004400142490000800186       2022                            d  c12/20211 aPiotr Pecka1 aMateusz Nowak00aRouting Algorithms Simulation for Self-Aware SDN  uhttps://www.mdpi.com/2079-9292/11/1/1040 v 1100280nas a2200109   4500000000100000000000100001008004100002260001200043100001600055245006600071490003300137       2006                            d  c06/20061 aPiotr Pecka00aSymulacja kolejki z negatywnymi klientami i wieloma serwerami0 v Vol. 27, No. 4 = No. 69 [5]00287nas a2200097   4500000000100000000000100001008004100002100002300043700002000066245010300086       2008                            d1 aPrzemysław Głomb1 aSławomir Nowak00aExperimental rate distortion analysis of image coder with contourlet transform and SPIHT algorithm00675nas a2200157   4500000000100000000000100001000000100002008004100003100001500044700002000059245006800079300001400147490000700161520033500168022001400503       2011                            d1 aM. Wróbel1 aSławomir Nowak00aModel of the network physical layer for modern wireless systems  a147–1590 v233 aIn the article the detailed and flexible model of physical layer PHY for discrete events simulations (DES) in proposed. The model was implemented using SimPy simulator and evaluated using simplified 802.11 protocol model. To proof the correctness of the model some results of selected 802.11 parameters verification are presented.  a1896-533400652nas a2200133   4500000000100000000000100001000000100002008004100003100001800044700002000062245008400082490000700166520034500173       2009                            d1 aMateusz Nowak1 aSławomir Nowak00aSynchronisation concept for distributed simulation of networks with packet loss0 v303 aWe propose a concept of a new synchronisation method of distributed event-driven simulation of computer networks with non-zero rate of lost packets (frames), e.g. wireless networks. Thanks to treating straggler messages (all or some of them) as messages informing of damaged frames, better performance of distributed simulation is expected.00326nas a2200145   4500000000100000000000100001000000100002008004100003100001800044700002000062245006300082300001400145490000700159022001400166       2007                            d1 aMateusz Nowak1 aSławomir Nowak00aParallel simulations with modified INET simulation package  a147–1560 v19  a1896-533400353nas a2200109   4500000000100000000000100001008004100002260003400043100002000077700002000097245012600117       2004                            d  bWydawnictwa Naukowo Technicze1 aSławomir Nowak1 aAndrzej Grzywak00aNanotechnologia i nanoinformatyka w Europie i na Swiecie, Rozdział w książce Internet w Społeczeństwie Informacyjnym00291nas a2200109   4500000000100000000000100001008004100002260005100043100002000094700002000114245004700134       2003                            d  bWyższa Szkoła Biznesu w Dąbrowie Górniczej1 aSławomir Nowak1 aAndrzej Grzywak00aInternet w społęczeństwie informacyjnym00328nas a2200121   4500000000100000000000100001008004100002260004300043100002000086700002000106700002000126245006000146       2005                            d  bWydawnictwa Komunikacji i Łączności1 aSławomir Nowak1 aAndrzej Grzywak1 aPiotr Pikiewicz00aNowe Technologie w Komputerowych Systemach Zarządzania01109nas a2200109   4500000000100000000000100001008004100002260005100043100002000094245008200114520080300196       2005                            d  c06bIITIS PANaUl. Bałtycka 5, 44-100 Gliwice1 aSławomir Nowak00aSystemy informatyki realizujące bezpośrednio procesy wytwarzania produktów3 aOrganizacji i porządkowaniu ruchu służą, zarówno w systemach technicznych jak i w organizmach biologicznych systemy informatyki. Obecnie, jako najbardziej znane, można wymienić dwa następujące systemy: - systemy informatyki wspomagające realizację procesów obliczeniowych, oparte na komputerach, które będziemy nazywać technicznymi systemami informatyki. - systemy informatyki zawarte w organizmach biologicznych, realizujące bezpośrednie wytwarzanie niektórych materiałów i produktów, potrzebnych tym organizmom, które będziemy nazywali biologicznymi systemami informatyki. Teza pracy: możliwa jest, poza organizmami biologicznymi, techniczna realizacja systemów informatyki służących do bezpośredniej realizacji niektórych, potrzebnych nam materiałów i produktów.00230nas a2200097   4500000000100000000000100001008004100002260000900043100002000052245006000072       2003                            d  bIEEE1 aSławomir Nowak00aTechnical nanosystems based on the biological solutions00267nas a2200133   4500000000100000000000100001000000100002008004100003100002000044245003400064300001400098490000700112022001400119       2003                            d1 aSławomir Nowak00aThe manufacturing nanosystems  a235–2440 v14  a0867-212100304nas a2200133   4500000000100000000000100001000000100002008004100003100002000044245007300064300001200137490000700149022001400156       2003                            d1 aSławomir Nowak00aModel systemu informatycznego wzorowanego na systemach biologicznych  a87–950 v2A  a0208-728600244nas a2200109   4500000000100000000000100001008004100002260000900043100002000052245004800072300001400120       2002                            d  bIEEE1 aSławomir Nowak00aThe nanosystems structures of manufacturing  a381–38400287nas a2200133   4500000000100000000000100001000000100002008004100003100002000044245005600064300001200120490000700132022001400139       2002                            d1 aSławomir Nowak00aKoncepcje realizacji procesów nanotechnologicznych  a45–580 v2A  a0208-728600276nas a2200121   4500000000100000000000100001000000100002008004100003100002000044245006200064300001400126022001400140       2001                            d1 aSławomir Nowak00aWykorzystanie mechanizmu RED w regulacji ruchu sieciowego  a131–143  a0208-728600270nas a2200133   4500000000100000000000100001000000100002008004100003100002000044245003700064300001400101490000700115022001400122       2001                            d1 aSławomir Nowak00aNanotechnology and Nanocomputers  a195–2040 v13  a0867-212101046nas a2200181   4500000000100000000000100001000000100002008004100003260000600044100002000050700002300070700001800093245010000111300001400211490000600225520061900231022001400850       2013                            d  c81 aSławomir Nowak1 aA. Debudaj-Grabysz1 aMateusz Nowak00aEvaluations of the new synchronization method for the parallel simulations of wireless networks  a698–7020 v53 aThe article describes the implementation of the new synchronization method for parallel discrete event simulation and presents the experimental results obtained from parallel environment. In the proposed method the time steps are introduced and the synchronization occurs only at the end of a time step to achieve reduction of messages, exchanged between local processes of simulation. The method was implemented in C++, using the MPI libraty. The studies have shown that it is possible, under certain conditions and using appropriate hardware architecture, the significant acceleration of distributed simulations.  a1793-820100901nas a2200121   4500000000100000000000100001008004100002100002000043700002200063700001800085245009900103520057700202       2012                            d1 aSławomir Nowak1 aAgnieszka Grabysz1 aMateusz Nowak00aEvaluation of the new synchronization method for the parallel simulations of wireless networks3 aThe article describes the implementation of the new synchronization method for parallel discrete event simulation and presents the experimental results obtained from parallel environment. The method, designed especially for wireless networks simulations, merges paradigms of optimistic and conservative synchronization to achieve reduction of messages, exchanged betweed local processes of simulation. The studies have shown that it is possible, under certain conditions and using appropriate hardware architecture, the significant acceleration of distributed simulations.00307nas a2200121   4500000000100000000000100001008004100002260002400043100002000067700002200087245006200109300001400171       2003                            d  bInformation Society1 aSławomir Nowak1 aKrzysztof Grochla00aSimple Traffic Shaping Algorithm for All-optical Networks  a413–41800351nas a2200145   4500000000100000000000100001000000100002008004100003100002000044700002200064245008400086300001400170490000700184022001400191       2003                            d1 aSławomir Nowak1 aKrzysztof Grochla00aWpływ mechanizmów kształtowania ruchu na wydajność sieci czysto optycznych  a221–2280 v2A  a0208-728600389nas a2200109   4500000000100000000000100001008004100002100002000043700002300063245010600086520008700192       2010                            d1 aSławomir Nowak1 aPrzemysław Głomb00aREMOTE VIRTUAL REALITY: EXPERIMENTAL INVESTIGATIONSOF PROGRESSIVE 3D MESH TRANSMISSION IN IP NETWORKS3 aPerformance Modelling and Evaluation of Heterogeneous Networks, proc. HETNET 2010.00319nas a2200121   4500000000100000000000100001008004100002260005900043100002000102700002400122245003700146300001400183       2001                            d  bWydawnictwo Pracowni Komputerowej Jacka Skalmierskiego1 aSławomir Nowak1 aStanisław Jędruś00aA Detailed ATM Network Simulator  a193–20200246nas a2200109   4500000000100000000000100001008004100002260000800043100002000051700001800071245004700089       2004                            d  bWNT1 aSławomir Nowak1 aMateusz Nowak00aWspółczesne problemy sieci komputerowych00490nas a2200157   4500000000100000000000100001000000100002008004100003100002000044700001800064245005100082300001400133490000700147520016400154022001400318       2004                            d1 aSławomir Nowak1 aMateusz Nowak00aSelforganization in nanosystems of informatics  a311–3200 v163 aIn the paper the problem of technical nanosystems of informatics able to creating compound structures with the use of selforganization phenomena is considered.  a0867-212101126nas a2200193   4500000000100000000000100001008004100002260003400043100002000077700001800097700001900115700002000134700002400154700001500178245006300193490000900256520064500265020002200910       2011                            d  bSpringeraBerlin / Heidelberg1 aSławomir Nowak1 aMateusz Nowak1 aPawel Foremski1 aSergey Balandin1 aKoucheryavy Yevgeni1 aHu Honglin00aSmart Spaces and Next Generation Wired/Wireless Networking0 v68693 aWe present a new synchronization method for parallel discrete event simulation for wireless networks. The method merges paradigms of time-stepped and event-driven simulations to achieve reduction of messages exchanged between local processes of simulation. The method is particularly suitable for wireless network simulation, in which objects share the same physical medium and parallel simulation involves a significant overhead for interprocess communication. The article describes the method and compare it to conservative and optimistic synchronization methods, on the basis of results of simulations of selected wireless network model.  a978-3-642-22874-201130nas a2200181   4500000000100000000000100001008004100002260005000043100002000093700001800113700002200131700002200153700001400175700001300189245007800202300001200280520065600292       2014                            d  bSpringer International Publishing Switzerland1 aSławomir Nowak1 aMateusz Nowak1 aKrzysztof Grochla1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aMAGANET - On the need of realistic topologies for AMI network simulations  a79–883 aThe article proposes MAGANET (Map-based Generator of AMI Network Topology), new map-based topology generator, designated for Advanced Metering Infrastructure (AMI) networks as well as for the urban wireless sensor networks (WSNs). The correct, reliable representation of network topology is crucial for the correct performance evaluation on large multihop wireless networks. Most of the current work us different random topologies. In the article we describe proposed new tool and its features. The comparison of the properties of topology provided by MAGANET for three different generation modes: uniform random, grid and map based is also presented.00325nas a2200121   4500000000100000000000100001008004100002260000900043100002000052700002000072245009700092300001400189       2003                            d  bIEEE1 aSławomir Nowak1 aStefan Węgrzyn00aReproduction and multiplication in the distributed, manufacturing nanosystems of informatics  a860–86200930nas a2200193   4500000000100000000000100001000000100002008004100003260000600044100002000050700002000070700001700090700002100107245006200128300001100190490000700201520051400208022001400722       2006                            d  c51 aSławomir Nowak1 aStefan Węgrzyn1 aJerzy Klamka1 aLech Znamirowski00aSystems of Informatics of the Direct Materials Production  a3–140 v183 aThe results of researches in the area of nanosystems of informatics, self-replication and self-organization processes are presented. Developing and joining together the gotten results into the integral whole permits in these three mentioned scopes to elaborate the scientific bases for the construction of structure of the algorithms and organization of nanosystems of informatics for the direct material production and object fabrication, and the inspiration of processes of the self-organization of objects.  a0867-212101371nas a2200157   4500000000100000000000100001000000100002008004100003100002000044700002000064700002100084245008600105300001400191520099400205022001401199       2005                            d1 aSławomir Nowak1 aStefan Węgrzyn1 aLech Znamirowski00aSelf-organization Process Inside the Set of Elements Inspired by their Properties  a231–2403 aSystems of technical informatics are being used for the realization of computational processes performed on the indicated data in a form of numbers. Computations are not performed in biological systems of informatics but the sets of technological operations which material products are coming into existence for living organism construction. It is possible to create technical systems of informatics which such goals will be realizing like in the case of biological systems and so will be taken to manufacturing objects and products on the basis of the molecular nanotechnology. It leads to three problems connected with the realization of creating technical systems of informatics and it concerns: the nanosystems of informatics, the self-replication, and the self-organization processes. In the paper, a problem of transient states of the process occurred for the self-organization of the object with properties of basic elements constituting this object determined, have been presented.  a0867-212101516nam a2200181   4500000000100000000000100001008004100002260008800043100002000131700002000151700002100171700001700192700002000209245006500229250000600294520101600300020001801316       2006                            d  bWyd. Pracowni Komputerowej Jacka Sklamierskiegoaul. Pszczyńska 44, 44-100 Gliwice1 aSławomir Nowak1 aStefan Węgrzyn1 aLech Znamirowski1 aJerzy Klamka1 aSławomir Nowak00aSystemy Informatyki Bezpośredniego Wytwarzania Materiałów  a13 aPrzedstawione opracowanie wynika z dającej się coraz wyraźniej odczuć potrzeby traktowania i rozwijania informatyki jako ogólnej dziedziny nauki o ruchu i przetwarzaniu informacji wszędzie tam, gdzie ruch i przetwarzanie informacji mają miejsce. Chodzi więc zarówno o różne systemy techniczne, jak i o organizmy biologiczne. Biologiczne systemy informatyki, rozwijające się od miliardów lat, charakteryzują się m.in. tym, że mają w nich miejsce procesy bezpośredniego wytwarzania potrzebnych organizmowi materiałów i produktów. Wzorując się na tym, autorzy niniejszego opracowania podjęli prace nad takimi technicznymi systemami informatyki, w których również mogły być realizowane procesy bezpośredniego wytwarzanie materiałów i produktów. Ponieważ z dotychczasowych naszych prac już obecnie wynika, że taka możliwość istnieje, postanowiliśmy zebrać nasze opracowania w pewną całość, ilustrującą jeden z obecnie podstawowych kierunków rozwojowych w informatyce.  a83-89105-97-700280nas a2200097   4500000000100000000000100001008004100002100002300043700002000066245009600086       2009                            d1 aPrzemysław Głomb1 aSławomir Nowak00aIMAGE CODING WITH CONTOURLET / WAVELET TRANSFORMS AND SPIHT ALGORITHM:AN EXPERIMENTAL STUDY00485nam a2200181   4500000000100000000000100001008004100002260004900043100002000092700001700112700002300129700001300152700002300165700002100188700002200209700002000231245005200251       2004                            d  bWydawnictwo Politechniki ŚląskiejaGliwice1 aStefan Węgrzyn1 aJerzy Klamka1 aSławomir Bugajski1 aM. Gibas1 aRyszard Winiarczyk1 aLech Znamirowski1 aJaroslaw Miszczak1 aSławomir Nowak00aNano i kwantowe systemy informatyki. Wyd.Drugie00503nam a2200193   4500000000100000000000100001008004100002260004900043100002000092700001700112700002300129700001300152700002300165700002100188700002200209700002000231245004000251020001800291       2003                            d  bWydawnictwo Politechniki ŚląskiejaGliwice1 aStefan Węgrzyn1 aJerzy Klamka1 aSławomir Bugajski1 aM. Gibas1 aRyszard Winiarczyk1 aLech Znamirowski1 aJaroslaw Miszczak1 aSławomir Nowak00aNano i Kwantowe Systemy Informatyki  a83-7335-295-300978nas a2200181   4500000000100000000000100001000000100002008004100003100002000044700001700064700002100081700002300102700002000125245004400145300001100189490000700200520058900207       2004                            d1 aStefan Węgrzyn1 aJerzy Klamka1 aLech Znamirowski1 aRyszard Winiarczyk1 aSławomir Nowak00aNano and quantum systems of informatics  a1–100 v523 aIn the present studies on quantum computer systems principles of a quantum logic gates and basic elements of quantum computers are described. The fundamental elements of quantum computer and mathematical backgrounds of suitable quantum computation processes are presented. Specially Toffoli quantum gate and Hadamard quantum gate are discussed in details. Mathematical bases and principles of quantum computations are also mentioned. Finally, the role of nuclear magnetic resonance in quantum computations is investigated. Moreover, the extensive list of references is also presented.00305nas a2200121   4500000000100000000000100001008004100002260000800043100002000051700001800071700002200089245007200111       2014                            d  cMay1 aSławomir Nowak1 aMateusz Nowak1 aKrzysztof Grochla00aProperties of advanced metering infrastructure networks' topologies00572nas a2200217   4500000000100000000000100001008004100002260001900043100002200062700002000084700001700104700002500121700001800146700002000164700002500184700002200209700002300231245006900254250001800323300001300341       2020                            d  bNow Publishers1 aGianmarco Baldini1 aPiotr Fröhlich1 aErol Gelenbe1 aJose Hernandez-Ramos1 aMateusz Nowak1 aSławomir Nowak1 aStavros Papadopoulos1 aAnastasios Drosou1 aDimitrios Tzovaras00aIoT Network Risk Assessment and Mitigation: The  SerIoT Approach  aJohn Soldatos  a88–10401468nas a2200157   4500000000100000000000100001008004100002100001900043700002000062700002000082700002500102700002200127245007700149490000700226520107700233       2020                            d1 aPawel Foremski1 aSławomir Nowak1 aPiotr Fröhlich1 aJose Hernandez-Ramos1 aGianmarco Baldini00aAutopolicy: Automated Traffic Policing for Improved IoT Network Security0 v203 a<p>A 2.3Tbps DDoS attack was recently mitigated by Amazon, which is a new record after the 2018 GitHub attack, or the famous 2016 Dyn DNS attack launched from hundreds of thousands of hijacked IoT devices. These attacks may disrupt the lives of billions of people worldwide, as we increasingly rely on the Internet. In this paper, we tackle the problem that hijacked IoT devices are often the origin of these attacks. With the goal of protecting the Internet and local networks, we propose Autopolicy: a system that automatically limits the IP traffic bandwidth - and other network resources - available to IoT devices in a particular network. We make use of the fact that devices such as sensors, cameras, and smart home appliances rarely need their high-speed network interfaces for normal operation. We present a simple yet flexible architecture for Autopolicy, specifying its functional blocks, message sequences, and general operation in a Software Defined Network. We present experimental validation results, and release a prototype open source implementation.</p>
02579nas a2200193   4500000000100000000000100001008004100002260001200043100002000055700001800075700002200093700002600115700001900141245011800160856004500278490000700323520204100330022001402371       2020                            d  c09/20201 aSławomir Nowak1 aMateusz Nowak1 aGianmarco Baldini1 aJosé Hernandez-Ramos1 aRicardo Neisse00aMitigation of Privacy Threats due to Encrypted Traffic Analysis through a Policy-Based Framework and MUD Profiles  uhttps://www.mdpi.com/2073-8994/12/9/15760 v123 a<p>It has been proven in research literature that the analysis of encrypted traffic with statistical analysis and machine learning can reveal the type of activities performed by a user accessing the network, thus leading to privacy risks. In particular, different types of traffic (e.g., skype, web access) can be identified by extracting time based features and using them in a classifier. Such privacy attacks are asymmetric because a limited amount of resources (e.g., machine learning algorithms) can extract information from encrypted traffic generated by cryptographic systems implemented with a significant amount of resources. To mitigate privacy risks, studies in research literature have proposed a number of techniques, but in most cases only a single technique is applied, which can lead to limited effectiveness. This paper proposes a mitigation approach for privacy risks related to the analysis of encrypted traffic which is based on the integration of three main components: (1) A machine learning component which proactively analyzes the encrypted traffic in the network to identify potential privacy threats and evaluate the effectiveness of various mitigation techniques (e.g., obfuscation), (2) a policy based component where policies are used to enforce privacy mitigation solutions in the network and (3) a network node profile component based on the Manufacturer Usage Description (MUD) standard to enable changes in the network nodes in the cases where the first two components are not effective in mitigating the privacy risks. This paper describes the different components and how they interact in a potential deployment scenario. The approach is evaluated on the public dataset ISCXVPN2016 and the results show that the privacy threat can be mitigated significantly by removing completely the identification of specific types of traffic or by decreasing the probability of their identification as in the case of VOIP by 50%, Chat by 40% and Browsing by 33%, thus reducing significantly the privacy risk.</p>
  a2073-899401877nas a2200169   4500000000100000000000100001008004100002260003800043100002000081700002200101700002200123700002100145245009300166856005000259300001400309520138400323       2023                            d  c07/2023bIEEEaMarrakesh, Morocco1 aSławomir Nowak1 aKrzysztof Grochla1 aMariusz Słabicki1 aMichał Gorawski00aEfficient Universal Indoor Multicharacteristic Propagation model for indoor environments  uhttps://ieeexplore.ieee.org/document/10183073  a1052-10583 a<p>Accurate indoor radio propagation modelling is a well-known problem that researchers tend to solve in many ways. Related methods differ in terms of accuracy and complexity. The article’s contribution is a new stochastic propagation model, Universal Indoor Multicharacteristic Propagation (UIMP), which results are statistically consistent with those measured in a real environment. The model could be used to verify real- world applications due to the low computational complexity and high accuracy level compared to state-of-the-art methods. This method can be especially beneficial for the efficient modelling of complex environments shared by many users using discrete events simulations (DES) based on a series of measurements in a real environment. In the article, we describe the method and present how to establish the model. The motivation was to develop an efficient method for use in an actual application: using the Bluetooth Low Energy (BLE) signal to estimate the position of users in indoor environments – the use of the proposed UIMP model allowed for a reliable assessment of the developed functionalities. Therefore we also present how the model was used to evaluate an example, real functionality, showing that the use of simplified models may falsify the assessment of the analyzed method and may lead e.g. to incorrect implementation assumptions.</p>
01311nas a2200145   4500000000100000000000100001008004100002260000900043100001900052700002000071700001800091245008200109856006500191520090900256       2025                            d  c20251 aMarzena Halama1 aSławomir Nowak1 aKonrad Połys00aSemantic Orientation for Indoor Navigation System using Large Language Models  uhttps://link.springer.com/article/10.1038/s41598-025-25281-03 a<p>Autonomous robots play an important role in modern indoor navigation, but existing systems often struggle with seamless human interaction and semantic understanding of environments. This paper presents an Artificial Intelligence (AI)-driven object recognition system enhanced by Large Language Models (LLMs), such as GPT-4 Vision and Gemini, to bridge this gap. Our approach combines vision-based mapping techniques with natural language processing and interactions to enable intuitive collaboration in solving navigation tasks. By leveraging multimodal input and vector space analysis, our system achieves enhanced object recognition, semantic embedding, and context-aware responses, setting a new standard for autonomous indoor navigation. This approach provides a novel framework for improving spatial understanding and dynamic interaction, making it suitable for complex indoor environments.</p>
00303nas a2200109   4500000000100000008004100001260001800042100002000060700001800080700002000098245007500118       2026                            d  aNaples, Italy1 aŁukasz Sobczak1 aNur Kelesoglu1 aSławomir Nowak00aRisk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning02040nas a2200121   4500000000100000008004100001100001500042700001900057700001600076700002000092245011200112520169400224       2026                            d1 aMert Nakip1 aRafał Gibała1 aAnna Grygar1 aSławomir Nowak00aTrend-based Multi-Modal Anomaly and Cyber Threat Detection with Traceable Explainability for Cloud Services3 a<p>Multi-modal real-time monitoring systems based on AI are<br>essential for ensuring system reliability and cyber-defense in cloud computing,<br>but are challenged by context isolation, early-fusion computational<br>overhead, and insufficient transparency. In order to address these<br>issues, this paper introduces T-MATE, an explainable Trend-based Multimodal<br>Anomaly and Threat detector engineered for robust, secure cloud<br>infrastructure monitoring. Rather than treating multi-modal streams<br>monolithically, T-MATE structurally decouples data modalities into independent<br>neural network heads, using a specialized Recurrent Trend<br>Predictive Neural Network (rTPNN) to isolate underlying trends and levels<br>across quantitative performance telemetry, while qualitatively parsing<br>textual event logs via Gemini 2.5 Flash. These components output<br>bounded anomaly scores and are integrated at the decision level<br>using an Empirical Reliability-Weighted Max Fusion operator, which<br>scales individual outputs to enforce a max-safety posture and eliminate<br>parameter-explosion liabilities. In order to demonstrate operational deployment<br>readiness, the framework is thoroughly evaluated via 10-fold<br>cross-validation and compared against standalone rTPNN, LSTM, and<br>MLP models alongside the baseline Avg-Fuse paradigm on the public<br>CloudAnoBench dataset. The results reveal that T-MATE achieves a superior<br>overall F1 Score of 0.88, a top threshold-invariant AUC-ROC of<br>0.96, an exceptional True Positive Rate of 0.96, and a processing footprint<br>that confirms its viability for close to real-time inference in enterprise<br>cloud infrastructures.</p>01933nas a2200109   4500000000100000008004100001100001500042700001900057700002000076245009100096520163600187       2026                            d1 aMert Nakip1 aRafał Gibała1 aSławomir Nowak00aOptimizing Information Retrieval in RAG Systems via Proactive Hyperparameter Selection3 a<p>Information retrieval is a crucial operation for Retrieval-<br>Augmented Generation (RAG) systems, providing substantial gains in<br>factual accuracy and operational eciency. However, the performance of<br>a RAG system is highly sensitive to the selection of hyperparameters, optimization of which is computationally expensive and time-consuming. In<br>this paper, we propose a novel framework, called Proactive Hyperparameter<br>Estimation and Selection (ProHES), which introduces a paradigm<br>shift from reactive, brute-force search to an ecient, analytical approach.<br>The core novelty of the ProHES framework is its two-stage methodology.<br>First, it analytically estimates the most suitable similarity metric<br>for a given embedding and dataset by quantifying intrinsic embedding<br>properties, i.e. sparsity, magnitude meaning, feature independence, and<br>value spread. Then, it systematically selects the optimal number of retrieved<br>documents based on user preferences and system requirements.<br>We evaluate the performance of the ProHES framework across six different<br>embedding functions on the MS Marco and TriviaQA datasets.<br>Our results demonstrate that ProHES provides a near-optimal hyperparameter<br>conguration with 22- to 86-fold reduction in computation<br>time compared to exhaustive search, achieving an average of 94% of the<br>best possible performance. The proposed framework represents a significant<br>advancement by making hyperparameter tuning for RAG systems<br>more ecient and systematic, thereby paving the way for more accurate,<br>robust and scalable real-world applications.</p>01166nas a2200157   4500000000100000000000100001008004100002260001200043100001700055700001400072245005000086300001400136490000600150520083800156022001400994       2016                            d  c03/20161 aErol Gelenbe1 aE.T Ceran00aEnergy Packet Networks With Energy Harvesting  a1321-13310 v43 a<p>We investigate the cooperation among energy prosumers (unified energy provider and consumer) through the energy packet network (EPN) paradigm, which represents both the flow of work that requires energy, and the flow of energy itself, in terms of discrete units. This paper details a stochastic model of EPNs, which is inspired from a branch of queuing theory called G-networks. The model allows us to compute the equilibrium state of a system that includes energy storage units, energy transmission networks, and energy consumers, together with the intermittent energy sources. The model is then used to show how the flow of work and energy in the system can be optimized for certain utility functions that consider both the needs of the consumers, and the desire to maintain some reserve energy for potential future needs.</p>
  a2169-353601493nas a2200169   4500000000100000000000100001008004100002260001200043100001100055700001100066700001700077245005300094300001200147490000700159520114200166022001501308       2016                            d  c02/20161 aO Brun1 aL Wang1 aErol Gelenbe00aBig Data for Autonomic Intercontinental Overlays  a575-5830 v343 a<p>This paper uses big data and machine learning for the real-time management of Internet scale quality-of-service (QoS) route optimisation with an overlay network. Based on the collection of data sampled every 2 min over a large number of source-destinations pairs, we show that intercontinental Internet protocol (IP) paths are far from optimal with respect to QoS metrics such as end-to-end round-trip delay. We, therefore, develop a machine learning-based scheme that exploits large scale data collected from communicating node pairs in a multihop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. Inspired from cognitive packet network protocol, it uses random neural networks with reinforcement learning based on the massive data that is collected, to select intermediate overlay hops. The routing scheme is illustrated on a 20-node intercontinental overlay network that collects some 2 × 106 measurements per week, and makes scalable distributed routing decisions. Experimental results show that this approach improves QoS significantly and efficiently</p>
  a0733-8716 01217nas a2200157   4500000000100000000000100001008004100002260002600043100001200069700001700081245005600098300000900154490000800163520086600171022002201037       2016                            d  bIEEEaBrunów, Poland1 aL. Wang1 aErol Gelenbe00aReal-Time Traffic over the Cognitive Packet Network  a3-210 v6083 a<p>Real-Time services over IP (RTIP) have been increasingly significant due to the convergence of data networks worldwide around the IP standard, and the popularisation of the Internet. Real-Time applications have strict Quality of Service (QoS) constraint, which poses a major challenge to IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement, and in this paper we design and experimentally evaluate the“Real-Time (RT) over CPN” protocol which uses QoS goals that match the needs of real-time packet delivery in the presence of other background traffic under varied traffic conditions. The resulting design is evaluated via measurements of packet delay, delay variation (jitter) and packet loss ratio.</p>
  a978-3-319-39206-601151nas a2200145   4500000000100000000000100001008004100002260003300043100001600076700001700092245006900109300000800178520079700186022002200983       2016                            d  bIEEEaKuala Lumpur, Malaysia1 aF. Francois1 aErol Gelenbe00aTowards a cognitive routing engine for software defined networks  a1-63 a<p>In Software Defined Networks (SDN), intensive traffic monitoring is used to optimize the Quality-of-Service (QoS) of the network paths which are selected. Thus, we introduce the use of the Cognitive Packet Network (CPN) algorithm to SDN in order to optimize the search for new high-QoS paths. We install the CPN algorithm in the Cognitive Routing Engine (CRE), a new application software for SDN, and show that with limited monitoring overhead we are able to determine the near-optimal paths for given QoS metrics that may be proposed by the end users. Measurements that we have conducted on an experimental replica of the GEANT network that our approach uses close to 10 times less monitoring data than conventional SDN, but that we are able to approach the optimal paths within 2%.</p>
  a978-1-4799-6664-600982nas a2200157   4500000000100000000000100001008004100002260002800043100001700071700002100088700001700109245006800126300000800194520060000202020002200802       2016                            d  bIEEEaKauai, Hawai, USA1 aErol Gelenbe1 aOmer Abdelrahman1 aGokce Gorbil00aDetection and mitigation of signaling storms in mobile networks  a1-53 a<p>Mobile Networks are subject to “signaling storms” launched by malware or apps, which overload the the bandwidth at the cell, the backbone signaling servers, and Cloud servers, and may also deplete the battery power of mobile devices. This paper<br />
reviews the subject and discusses a novel technique to detect and mitigate such signaling storms. Through a mathematical analysis we introduce a technique based on tracking time-out transitions in the signaling system that can substantially reduce both the number of misbehaving mobiles and the signaling overload in the backbone.</p>
  a978-1-4673-8579-401591nas a2200157   4500000000100000000000100001008004100002260003500043100001500078700001700093245008000110300001200190490000800202520120200210020002101412       2016                            d  bSpringeraThessaloniki, Greece1 aW. Serrano1 aErol Gelenbe00aAn Intelligent Internet Search Assistant Based on the Random Neural Network  a141-1530 v4753 a<p>Even web services that are free of charge, typically offer access to online information based on some form of economic interest of the web service itself. Thus advertisers who put the information on the web will make a payment to the search services based on the clicks that their advertisements receive. Thus end users cannot know that the results they obtain from Web search engines are exhaustive, or that they actually respond to their needs. To fill the gap between user needs and the information that is presented to them on the web, Intelligent Search Assistants have been proposed to act at the interface between users and search engines to present data to users in a manner that reflects their actual needs or their observed or stated preferences. This paper presents an Intelligent Internet Search Assistant based on the Random Neural Network that tracks the user’s preferences and makes a selection on the output of one or more search engines using the preferences that it has learned. We also introduce a “relevance metric” to compare the performance of our Intelligent Internet Search Assistant against a few search engines, showing that it provides better performance.</p>
  a78-3-319-44943-201397nas a2200145   4500000000100000000000100001008004100002260004800043100002100091700001700112245005700129300001200186520103100198020002201229       2016                            d  bIEEEaImperial Coll Campus, London, England1 aOmer Abdelrahman1 aErol Gelenbe00aA Diffusion Model for Energy Harvesting Sensor Nodes  a154-1583 a<p>Energy harvesting has recently attracted much interest due to the emergence of the Internet of Things, and the increasing need to operate wireless sensing devices in challenging environments without much human intervention and maintenance. This paper presents a novel approach for modeling the performance of an energy harvesting wireless sensor node, which takes into account fluctuations in the amount of energy extracted from the environment, energy loss due to battery leakage, as well as the energy cost of sensing, data processing and communication. The proposed approach departs from the common queueing-theoretic framework used in the literature, and instead uses Brownian motion to represent more accurately the time evolution of the distribution of the node's battery level. The paper derives some performance measures of interest along with the stationary solution of the system, and discusses possible directions for reducing the number of parameters and states of the model without compromising accuracy.</p>
  a978-1-5090-3432-101533nas a2200145   4500000000100000000000100001008004100002260004800043100001600091700001700107245009500124300001200219520113400231022002201365       2016                            d  bIEEEaImperial Coll Campus, London, England1 aF. Francois1 aErol Gelenbe00aOptimizing Secure SDN-Enabled Inter-Data Centre Overlay Networks through Cognitive Routing  a283-2883 a<p>More and more businesses are deploying their application(s) with different cloud providers which are close to their customers in order to provide better Quality of Service (QoS) to their end customers. In this work, an optimized and secure software-defined overlay network is proposed as an efficient mechanism to interconnect these geographically-dispersed applications compared to using only plain IP routing. A logically centralized Cognitive Routing Engine (CRE), based on Random Neural Networks with Reinforcement Learning, was developed to find with minimal monitoring overhead the optimal overlay paths when the public Internet is used as the communication means between the overlay nodes. CRE was evaluated by using an overlay network composed of hosts from 5 different public clouds where it was shown that the latency of CRE paths is most of the time within 5% of the latency of the optimal IP paths. Furthermore, it was also demonstrated that CRE is able to do asymmetric path optimization where the forward path is different from the reverse path for a given data centre pair in order to further improve QoS.</p>
  a978-1-5090-3432-101151nas a2200145   4500000000100000000000100001008004100002260003700043100001700080700001000097245004600107300001400153520081600167022002200983       2016                            d  c06/2016bIEEEaVancouver, Canada1 aErol Gelenbe1 aY Yin00aDeep learning with random neural networks  a1633-16383 a<p>This paper introduces techniques for Deep Learning in conjunction with spiked random neural networks that closely resemble the stochastic behaviour of biological neurons in mammalian brains. The paper introduces clusters of such random neural networks and obtains the characteristics of their collective behaviour. Combining this model with previous work on extreme learning machines, we develop multilayer architectures which structure Deep Learning Architectures a a “front end” of one or two layers of random neural networks, followed by an extreme learning machine. The approach is evaluated on a standard - and large - visual character recognition database, showing that the proposed approach can attain and exceed the performance of techniques that were previously reported in the literature.</p>
  a978-1-5090-0620-500993nas a2200157   4500000000100000000000100001008004100002260002800043100001100071700001100082700001700093245006300110300001400173520062500187022002300812       2016                            d  bIEEEaBudapest, Hungary1 aL Wang1 aO Brun1 aErol Gelenbe00aAdaptive workload distribution for local and remote Clouds  a3984-39883 a<p>Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet. This paper describes a smart distributed system that combines local and remote Cloud facilities. It operates with a task allocation system that takes decisions to allocate tasks dynamically to the service that offers the best overall Quality of Service and a routing overlay which optimizes network delay for data transfer between clouds. Internet-scale experiments exhibit the effectiveness of our approach in adaptively distributing workload across multiple clouds.</p>
  a978-1-5090-1897-0 01332nas a2200145   4500000000100000000000100001008004100002260001200043100001700055245004300072300001000115490000700125520104000132022001401172       2016                            d  c09/20161 aErol Gelenbe00aAgreement in Spins and Social Networks  a15-170 v443 a<p>We introduce a model of mutually influencing individuals who hold one of two opinions about some matter. This is similar to a system of particles that can have one of two spin states. The model considers N sub-systems where individuals stay for some time, and then move from one subnetwork to another independently of each other, or they may leave the overall network from any one of the sub-systems. They arrive externally to any sub-system according to independent Poisson processes. In each sub-system individuals can influence each other to align with their own opinion or spin, and their opinion can also fluctuate at random in either opinion. The system also allows for a bias or directional field in any of the sub-systems that influences the individuals or spins that are locally present. We show that even with a weak bias, when random fluctuations become small then all the individuals or spins in a given sub-network will align with probability one, to the opinion or spin direction represented by the bias or spin.</p>
  a0163-599901593nas a2200169   4500000000100000000000100001008004100002100001700043700002100060700001600081700001700097245007200114300001200186490000600198520120500204022001401409       2016                            d1 aGokce Gorbil1 aOmer Abdelrahman1 aM. Pavloski1 aErol Gelenbe00aModeling and Analysis of RRC-Based Signalling Storms in 3G Networks  a113-1270 v43 a<p>Mobile networks are vulnerable to signaling attacks and storms that are caused by traffic patterns that overload the control plane, and differ from distributed denial of service attacks in the Internet since they directly affect the control plane, and also reserve wireless bandwidth and network resources without actually using them. Such storms can result from malware and mobile botnets, as well as from poorly designed applications, and can cause service outages in 3G and 4G networks, which have been experienced by mobile operators. Since the radio resource control (RRC) protocol in the 3G and 4G networks is particularly susceptible to such storms, we analyze their effect with a mathematical model that helps to predict the congestion that is caused by a storm. A detailed simulation model of a mobile network is used to better understand the temporal dynamics of user behavior and signaling in the network and to show how RRC-based signaling attacks and storms cause significant problems in both the control and user planes of the network. Our analysis also serves to identify how storms can be detected, and to propose how system parameters can be chosen to mitigate their effect.</p>
  a2168-675000457nam a2200169   4500000000100000000000100001008004100002260001300043100002100056700001700077700001700094700001700111245012100128300000800249490000800257020002200265       2016                            d  bSpringer1 aOmer Abdelrahman1 aErol Gelenbe1 aGokce Gorbil1 aRicardo Lent00aInformation Sciences and Systems 2015 30th International Symposium on Computer and Information Sciences (ISCIS 2015)  a4570 v363  a978-3-319-22634-700437nas a2200181   4500000000100000000000100001008004100002260000800043100001000051700001700061700001300078700001300091700001100104245010500115300001400220490000700234022001400241       2017                            d  cNov1 aJ. Du1 aErol Gelenbe1 aC. Jiang1 aH. Zhang1 aY. Ren00aContract Design for Traffic Offloading and Resource Allocation in Heterogeneous Ultra-Dense Networks  a2457-24670 v35  a0733-871600364nas a2200145   4500000000100000000000100001008004100002260004400043100001100087700001700098245006200115300001000177490000900187022002200196       2017                            d  c05/2017bIEEEaAnchorage, United States1 aY. Yin1 aErol Gelenbe00aSingle-cell based random neural network for deep learning  a86-930 v2017  a978-1-5090-6182-200439nas a2200157   4500000000100000000000100001008004100002260004400043100001100087700001200098700001700110245010900127300001400236490000900250022002200259       2017                            d  c05/2017bIEEEaAnchorage, United States1 aY. Yin1 aL. Wang1 aErol Gelenbe00aMulti-layer neural networks for quality of service oriented server-state classification in cloud servers  a1623-16270 v2017  a978-1-5090-6182-200359nas a2200157   4500000000100000000000100001008004100002260001200043100001700055700001900072700001700091245006000108300001200168490000700180022001400187       2017                            d  c06/20171 aH. Liu Jiang1 aRK. Thulasiram1 aErol Gelenbe00aSpecial Issue on Green Pervasive and Ubiquitous Systems  a806-8120 v11  a1932-818400299nas a2200121   4500000000100000000000100001008004100002260003000043100001800073700001700091245004900108020002000157       2017                            d  c2017bIEEEaParis, France1 aY.M. Kadioglu1 aErol Gelenbe00aWireless sensor with data and Energy Packets  a978-150901897-000356nas a2200133   4500000000100000000000100001008004100002260004600043100001800089700001700107700001700124245005800141020002300199       2017                            d  c2017bSpringeraKazan, Russian Federation1 aE. Pankratova1 aM. Farkhadov1 aErol Gelenbe00aResearch of Heterogeneous Queueing System SM|M(n)|∞  a978-3-319-68068-2 00289nas a2200145   4500000000100000000000100001008004100002260001200043100001500055700001700070245002700087300000800114490000600122022001500128       2017                            d  c07/20171 aJ Fourneau1 aErol Gelenbe00aG-networks with Adders  a1-70 v9  a1999-5903 00346nas a2200145   4500000000100000000000100001008004100002260001200043100001500055700001700070245007700087300001300164490000800177022001500185       2018                            d  c03/20181 aW. Serrano1 aErol Gelenbe00aThe Random Neural Network in a neurocomputing application for Web search  a123-134 0 v280  a0925-2312 00309nas a2200145   4500000000100000000000100001008004100002260001200043100001200055700001700067245004700084300001100131490000600142022001500148       2018                            d  c03/20181 aL. Wang1 aErol Gelenbe00aAdaptive Dispatching of Tasks in the Cloud  a33-45 0 v6  a2168-7161 00303nas a2200121   4500000000100000000000100001008004100002260003200043100001500075700001700090245005100107020002300158       2017                            d  c03/2018bIEEE aLondon, UK 1 aW. Serrano1 aErol Gelenbe00aIntelligent search with deep learning clusters  a978-1-5090-6435-9 00312nas a2200121   4500000000100000000000100001008004100002260004500043100001700088700001000105245005200115020002300167       2017                            d  c09/2017bSpringer, ChamaKraków, Poland1 aErol Gelenbe1 aY Yin00aDeep Learning with Dense Random Neural Networks  a978-3-319-67792-7 00336nas a2200121   4500000000100000000000100001008004100002260004600043100001500089700001700104245007000121020002300191       2017                            d  c2017bSpringer, ChamaVilamoura, Portugal1 aW. Serrano1 aErol Gelenbe00aThe Deep Learning Random Neural Network with a Management Cluster  a978-3-319-59423-1 00441nas a2200169   4500000000100000000000100001008004100002260004100043100000900084700001700093700001200110700001200122700001000134700001000144245009400154020002300248       2017                            d  c01/2018bIEEEaSingapore, Singapore 1 aJ Du1 aErol Gelenbe1 aC Jiang1 aH Zhang1 aZ Han1 aY Ren00aData Transaction Modeling in Mobile Networks: Contract Mechanism and Performance Analysis  a978-1-5090-5019-2 01633nas a2200217   4500000000100000000000100001008004100002260001200043100002200055700002000077700002000097700002000117700002400137700001700161700001700178245006300195300001000258490000800268520112500276022001401401       2018                            d  c08/20181 aMariacarla Staffa1 aLuigi Sgaglione1 aGiovanni Mazzeo1 aLuigi Coppolino1 aSalvatore D'Antonio1 aLuigi Romano1 aErol Gelenbe00aAn OpenNCP-based Solution for Secure eHealth Data Exchange  a65-850 v1163 a<p>The European Commission is very focused on the development of possible solutions to allow effective cross border healthcare provisioning with the aim of guaranteeing a uniform Quality of Service (QoS) level of healthcare systems across Europe. One of the most relevant efforts in this direction was the epSOS Project, with the release of the OpenNCP platform overcoming interoperability issues in health information exchange between nations while complying with both National and European laws. Despite the OpenNCP platform has been adopted in several Member States, allowing them to securely interconnect their eHealth infrastructures, some security issues are still partially addressed, This work shows how the KONFIDO project will address these issues by building on and extending the results of OpenNCP through a sound holistic approach to security at a systemic level. This paper describes in detail the KONFIDO project approach and how it is being deployed via a combination of complementary security enhancing technologies with the ultimate goal of increasing trust and security of eHealth data exchange.</p>
  a1084-804501743nas a2200157   4500000000100000000000100001008004100002260001200043100001900055700001700074245007200091300000900163490000700172520139100179022001501570       2018                            d  c08/20181 aYasin Kadioglu1 aErol Gelenbe00aProduct-Form Solution for Cascade Networks With Intermittent Energy  a1-100 vPP3 a<p>The power needs of digital devices, their installation in locations where it is difficult to connect them to the power grid and the difficulty of frequently replacing batteries, create the need to operate digital systems with harvested energy. In such cases, local storage batteries must overcome the intermittent nature of the energy supply. System performance then depends on the intermittent energy supply, possible energy leakage, and system workload. Queueing networks with product-form solution (PFS) are standard tools for analyzing the performance of interconnected systems, and predicting relevant performance metrics including job queue lengths, throughput, and system turnaround times and delays. However, existing queueing network models assume unlimited energy availability, whereas intermittently harvested energy can affect system performance due to insufficient energy supply. Thus, this paper develops a new PFS for the joint probability distribution of energy availability, and job queue length for an N-node tandem system. Such models can represent production lines in manufacturing systems, supply chains, cascaded repeaters for optical links, or a data link with multiple input data ports that feeds into a switch or server. Our result enables the rigorous computation of the relevant performance metrics of such systems operating with intermittent energy.</p>
  a1937-9234 01501nas a2200133   4500000000100000000000100001008004100002260004100043100001700084700001900101245007500120520114900195020002301344       2018                            d  c07/2018bIEEEaKansas City, MO, USA 1 aErol Gelenbe1 aYasin Kadioglu00aEnergy life-time of wireless nodes with network attacks and mitigation3 a<p>In the Internet of Things (IoT), a simple form of attack can deplete the energy available to operate the sensor nodes. Some of these nodes may use batteries, while others may harvest ambient energy such as photovoltaic, or electromagnetic, or vibration based energy. We first briefly survey the types of attacks which aim at the nodes' energy provisioning systems. This paper analyses the effect of such attacks on the energy life- time of a wireless node. Then we provide models to estimate the effect of attacks that attempt to deplete the node's energy supply, both for a node that uses energy harvesting. We then examine a simple means of attack mitigation based on dropping both attack and «good» traffic. For nodes that use energy harvesting, we compute the fraction of traffic that must be dropped so as to offer a desired «energy life-time» of the node. We see that the required traffic drop rate depends in a non-linear manner on the nominal «good traffic rate» at which the node is expected to operate. Finally, we analyse the impact of attacks on the energy life-time of a node that operates with a replaceable battery</p>
  a978-1-5386-4328-0 00454nas a2200145   4500000000100000000000100001008004100002260004400043100001700087700001600104700001700120245011300137520004400250020001400294       2018                            d  c07/2018bElsevieraGran Canaria, Spain 1 aOlivier Brun1 aYonghua Yin1 aErol Gelenbe00aDeep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments3 a<div class="author">Yonghua Yin</div>
  a1877-050901021nas a2200181   4500000000100000000000100001008004100002260004600043100001700089700001600106700001700122700002200139700002800161700001700189245011400206520049900320020002000819       2018                            d  c07/2018bSpringeraLondon, United Kingdom1 aOlivier Brun1 aYonghua Yin1 aErol Gelenbe1 aY. Murat Kadioglu1 aJavier Augusto-Gonzalez1 aManuel Ramos00aDeep learning with dense random neural networks for detecting attacks against IoT-connected home environments3 a<p>In this paper, we analyze the network attacks that can be launched against IoT gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We also present the principles and design of a deep learning-based approach using dense random neural networks (RNN) for the online detection of network attacks. Empirical validation results on packet captures in which attacks were inserted show that the Dense RNN correctly detects attacks.</p>
  a978-331995188-101430nas a2200205   4500000000100000000000100001008004100002260004600043100002200089700002000111700002000131700001700151700002000168700002100188700001400209700001900223245006600242520089400308020002201202       2018                            d  c07/2018bSpringeraLondon, United Kingdom1 aMariacarla Staffa1 aLuigi Coppolino1 aLuigi Sgaglione1 aErol Gelenbe1 aIoannis Komnios1 aEvangelos Grivas1 aOana Stan1 aLuigi Castaldo00aKONFIDO: An OpenNCP-Based Secure eHealth Data Exchange System3 a<p>Allowing cross-border health-care data exchange by establishing a uniform QoS level of health-care systems across European states, represents one of the current main goals of the European Commission. For this purpose epSOS project was funded with the objective to overcome interoperability issues in patients health information exchange among European healthcare systems. A main achievement of the project was the OpenNCP platform. Settled over the results of the epSOS project, KONFIDO aims at increasing trust and security of eHealth data exchange by adopting a holistic approach, as well as at increasing awareness of security issues among the healthcare community. In this light, the paper describes the KONFIDO project’s approach and discusses its design and its representation as a system of interacting agents. It finally discusses the deployment of the provided platform</p>
  a978-3-319-95189-801797nas a2200157   4500000000100000000000100001008004100002260004600043100002200089700001700111700002300128700002300151245006900174520137400243020002201617       2018                            d  c07/2018bSpringeraLondon, United Kingdom1 aMiltiadis Siavvas1 aErol Gelenbe1 aDionysios Kehagias1 aDimitrios Tzovaras00aStatic Analysis-Based Approaches for Secure Software Development3 a<p>Software security is a matter of major concern for software development enterprises that wish to deliver highly secure software products to their customers. Static analysis is considered one of the most effective mechanisms for adding security to software products. The multitude of static analysis tools that are available provide a large number of raw results that may contain security-relevant information, which may be useful for the production of secure software. Several mechanisms that can facilitate the production of both secure and reliable software applications have been proposed over the years. In this paper, two such mechanisms, particularly the vulnerability prediction models (VPMs) and the optimum checkpoint recommendation (OCR) mechanisms, are theoretically examined, while their potential improvement by using static analysis is also investigated. In particular, we review the most significant contributions regarding these mechanisms, identify their most important open issues, and propose directions for future research, emphasizing on the potential adoption of static analysis for addressing the identified open issues. Hence, this paper can act as a reference for researchers that wish to contribute in these subfields, in order to gain solid understanding of the existing solutions and their open issues that require further research.</p>
  a978-3-319-95189-800225nas a2200097   4500000000100000000000100001008004100002100001700043700001800060245004900078       2019                            d1 aErol Gelenbe1 aYunxiao Zhang00aPerformance Optimization With Energy Packets00316nas a2200121   4500000000100000000000100001008004100002260001200043100001900055700002200074700001700096245008100113       2019                            d  c05/20191 aKhaled Hussain1 aMohamed Bassyouni1 aErol Gelenbe00aAccurate, Energy-efficient Classification With Spiking Random Neural Network01212nas a2200133   4500000000100000000000100001008004100002260001200043100002200055700001700077245004800094490000700142520092900149       2019                            d  c07/20191 aMiltiadis Siavvas1 aErol Gelenbe00aOptimum checkpoints for programs with loops0 v973 a<p>Checkpoints are widely used to improve the performance of computer systems and programs inthe presence of failures, and they significantly reduce the overall cost of running a program, if the program or the underlying system, are subject to failures. Thus application level checkpointing has been proposed for programs which may execute on platforms which are prone to failures, and also to reduce the execution time of programs which are prone to internal failures. This paper develops a mathematical model to estimate the average execution time of a program in the presence of failures, without and with application level checkpointing, and we use it to predict the optimum interval number of instructions which should be executed between the placement of successive heckpoints. The case of programs with loops and nested loops is also discussed. The results are illustrated with several numerical examples.&nbsp;</p>
01283nas a2200121   4500000000100000000000100001008004100002260003200043100002000075700001700095245010900112520094000221       2020                            d  c06/2020bSpringeraZakopane1 aPiotr Fröhlich1 aErol Gelenbe00aOptimal fog services placement in SDN IoT network using Random Neural Networks and Cognitive Network Map3 a<p>Due to massive increase in number of <em>IoT</em>&nbsp;devices and number of cloud based services a crucial task arises of optimally placing (both topologically and resource-wise ) services in the network so that non of the clients will be victimized and receive best possible time of response. Also - there must be a balance not to instantiate a service on every possible machine - which would take too much resources. Task which must be solved is an optimization of parameters such as <em>QoS</em>&nbsp;between service and client, equality of clients and usage of resources. Using <em>SDN</em>&nbsp;- which is designed to answer some of the problems posed in this section such as <em>QoS</em>&nbsp;and knowledge about topology of the whole network and newly connected clients - is a gateway to better fit service management. Machine learning provides less stiff rules to follow and more intelligent behaviour of the manager.</p>
01382nas a2200193   4500000000100000000000100001008004100002260003300043100001700076700002000093700001800113700002500131700002600156700002100182700002300203245004800226856008600274520082800360       2020                            d  cJune 2020aBudva, Montenegro1 aErol Gelenbe1 aPiotr Fröhlich1 aMateusz Nowak1 aStavros Papadopoulos1 aAikaterini Protogerou1 aAnastasis Drosou1 aDimitrios Tzovaras00aIoT Network Attack Detection and Mitigation  uhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9134241&isnumber=91340633 aCyberattacks on the Internet of Things (IoT) can cause major economic and physical damage, and disrupt production lines, manufacturing processes, supply chains, impact the physical safety of vehicles, and damage the health of human beings. Thus we describe and evaluate a distributed and robust attack detection and mitigation system for network environments where communicating decision agents use Graph Neural Networks to provide attack alerts. We also present an attack mitigation system that uses a Reinforcement Learning driven Software Defined Network to process the alerts generated by the attack detection system, together with Quality of Service measurements, so as to re-route sensitive traffic away from compromised network paths using. Experimental results illustrate both the detection and re-routing scheme.
01121nas a2200121   4500000000100000000000100001008004100002260001800043100001700061700001600078245007900094520082600173       2020                            d  c05/2020bIEEE1 aErol Gelenbe1 aM. Pavloski00aPerformance of a Security Control Scheme for a Health Data Exchange System3 a<p>We propose an access control scheme to improve the security of a cross border health data access running under distributed systems such as OpenNCP that cooperate through message exchanges. The scheme allows messages that request data and services to cross different regional or national nodes after they are verified for security issues both by the sender and the receiver. It rejects those access requests that are detected to be malicious, and consequently throttles down the flow of permit messages via explicit feedback from threat detection software placed at the sender and the receiver. An analytical model is used to evaluate the message delay to determine the overhead caused by the enhanced security system, and numerical results illustrate the impact of attack rates and on the system’s performance.</p>
01134nas a2200157   4500000000100000000000100001008004100002260003700043100002000080700001700100700001800117245004100135856004900176520072900225020002200954       2020                            d  cJune 2020bIEEEaDublin, Ireland1 aPiotr Fröhlich1 aErol Gelenbe1 aMateusz Nowak00aSmart SDN Management of Fog Services  uhttps://ieeexplore.ieee.org/document/91195423 aA Smart Service Manager is proposed to direct user requests (such as those coming from IoT devices) at the edge
towards appropriate servers where the requested services can be satisfied. Services are housed at different Fog locations, and the system is subject to variations in workload. The approach is based on using a Software Defined Network (SDN) controller as the means to take decisions with measurement data based machine learning that uses Reinforcement Learning to make the best choices. The system we have developed is illustrated with experimental results on a test-bed with time-varying loads that confirm its ability to adapt to significant changes in system load and preserve the users’ Quality of Service.  a978-1-7281-6728-200336nas a2200109   4500000000100000000000100001008004100002100001700043245014500060300001500205490000600220       2020                            d1 aErol Gelenbe00aIntroduction to the Special Issue on the French-Polish Collaboration in Mathematical Models of Computer Systems, Networks and Bioinformatics  a44:1-44:100 v100901nas a2200157   4500000000100000000000100001000000100002008004100003260001900044100001700063700001800080245004800098250002900146520054600175022002200721       2020                            d  bSpringeraCham1 aErol Gelenbe1 aYunxiao Zhang00aSharing Energy for Optimal Edge Performance  aChatzigeorgiou A. et al.3 aUsing the Energy Packet Network (EPN) model, we show how energy can be shared between heterogeneous servers at the edge to minimize the overall average response time of jobs. The system is modelled as a probabilistic network where energy and jobs are being dispatched to the edge servers using G-Networks with a product-form solution for the equilibrium probability distribution of system state. The approach can also be used to design energy dispatching systems when renewable energy is used to improve the sustainability of edge computing.  a978-3-030-38918-500700nas a2200253   4500000000100000000000100001008004100002100001600043700001800059700002100077700001700098700002300115700001700138700001900155700001800174700002300192700001700215700002200232700002300254700002200277245009400299856004600393490000700439       2019                            d1 aMarco Nalin1 aIlaria Baroni1 aGiuliana Faiella1 aMaria Romano1 aFlavia Matrisciano1 aErol Gelenbe1 aDavid Martinez1 aJos Dumortier1 aPantelis Natsiavas1 aKostas Votis1 aVassilis Koutkias1 aDimitrios Tzovaras1 aFabrizio Clemente00aThe European cross-border health data exchange roadmap: Case study in the Italian setting  uhttps://doi.org/10.1016/j.jbi.2019.1031830 v9401082nas a2200409   4500000000100000000000100001008004100002100001900043700001900062700002000081700002200101700001900123700002100142700001700163700001800180700001700198700001600215700001500231700002000246700002100266700002500287700002400312700002000336700001800356700001700374700002500391700001400416700001600430700001200446700002200458700001800480700001700498245009500515856003600610300001900646490000700665       2019                            d1 aRajkumar Buyya1 aSatish Srirama1 aGiuliano Casale1 aRodrigo Calheiros1 aYogesh Simmhan1 aBlesson Varghese1 aErol Gelenbe1 aBahman Javadi1 aLuis Vaquero1 aMarco Netto1 aAdel Toosi1 aMaria Rodriguez1 aIgnacio Llorente1 aSabrina di Vimercati1 aPierangela Samarati1 aDejan Milojicic1 aCarlos Varela1 aRami Bahsoon1 aMarcos de Assunção1 aOmer Rana1 aWanlei Zhou1 aHai Jin1 aWolfgang Gentzsch1 aAlbert Zomaya1 aHaiying Shen00aA Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade  uhttps://doi.org/10.1145/3241737  a105:1–105:380 v5100353nas a2200133   4500000000100000000000100001008004100002100001900043700001700062245007200079856004700151300001400198490000700212       2019                            d1 aYasin Kadioglu1 aErol Gelenbe00aProduct-Form Solution for Cascade Networks With Intermittent Energy  uhttps://doi.org/10.1109/JSYST.2018.2854838  a918–9270 v1300494nas a2200181   4500000000100000000000100001008004100002100001100043700001700054700001900071700001700090700001300107700002000120245010300140856004600243300001600289490000700305       2019                            d1 aJun Du1 aErol Gelenbe1 aChunxiao Jiang1 aHaijun Zhang1 aYong Ren1 aH. Vincent Poor00aPeer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks  uhttps://doi.org/10.1109/TIFS.2018.2883000  a1582–15940 v1400476nas a2200181   4500000000100000000000100001008004100002100001100043700001900054700001700073700001700090700001300107700001400120245009200134856004500226300001600271490000700287       2019                            d1 aJun Du1 aChunxiao Jiang1 aErol Gelenbe1 aHaijun Zhang1 aYong Ren1 aTony Quek00aDouble Auction Mechanism Design for Video Caching in Heterogeneous Ultra-Dense Networks  uhttps://doi.org/10.1109/TWC.2019.2895076  a1669–16830 v1800666nas a2200229   4500000000100000000000100001008004100002260000900043100002800052700002200080700001900102700002300121700002400144700003000168700002300198700002300221700001300244700001700257700001700274245009800291856004700389       2019                            d  bIEEE1 aJavier Augusto-Gonzalez1 aAnastasija Collen1 aS. Evangelatos1 aM. Anagnostopoulos1 aGeorgios Spathoulas1 aKonstantinos Giannoutakis1 aKonstantinos Votis1 aDimitrios Tzovaras1 aB. Genge1 aErol Gelenbe1 aNiels Nijdam00aFrom Internet of Threats to Internet of Things: A Cyber Security Architecture for Smart Homes  uhttps://doi.org/10.1109/CAMAD.2019.885849300351nas a2200133   4500000000100000000000100001008004100002260000900043100002200052700001700074700001600091245005500107856005500162       2019                            d  bIEEE1 aMiltiadis Siavvas1 aErol Gelenbe1 aMeikang Qiu00aOptimum Interval for Application-level Checkpoints  uhttps://doi.org/10.1109/CSCloud/EdgeCom.2019.000-400468nas a2200169   4500000000100000000000100001008004100002260000900043100001700052700002200069700002000091700002100111700001800132700001700150245007900167856005200246       2019                            d  bIEEE1 aErol Gelenbe1 aFloriano De Rango1 aCarlos Calafate1 aMiroslav Voznák1 aAlfredo Garro1 aMauro Tropea00aEnergy Consumption and Quality of Service in Computer Systems and Networks  uhttps://doi.org/10.1109/DS-RT47707.2019.895867300967nas a2200361   4500000000100000000000100001008004100002260000900043100002700052700001600079700001800095700002200113700002100135700002300156700002100179700002000200700001700220700001500237700002400252700002400276700001700300700001800317700001800335700001700353700002000370700002200390700001700412700001400429700002100443700001900464245007800483856004400561       2019                            d  bIEEE1 aSotiris Diamantopoulos1 aMarco Nalin1 aIlaria Baroni1 aFabrizio Clemente1 aGiuliana Faiella1 aCharis Mesaritakis1 aEvangelos Grivas1 aJanne Rasmussen1 aJan Petersen1 aIsaac Cano1 aElisa Puigdomènech1 aDimitris Karamitros1 aErol Gelenbe1 aJos Dumortier1 aMaja Voronkov1 aLuigi Romano1 aLuigi Coppolino1 aVassilis Koutkias1 aKostas Votis1 aOana Stan1 aPaolo Campegiani1 aDavid Martinez00aSecure Cross-Border Exchange of Health Related Data: The KONFIDO Approach  uhttps://doi.org/10.1109/EDCC.2019.0002400513nas a2200169   4500000000100000000000100001008004100002260000900043100002500052700002400077700002200101700001700123700001600140700001800156245011700174856005200291       2019                            d  bIEEE1 aAlexander Frötscher1 aBernhard Monschiebl1 aAnastasios Drosou1 aErol Gelenbe1 aMartin Reed1 aMays Al-Naday00aImprove cybersecurity of C-ITS Road Side Infrastructure Installations: the SerIoT - Secure and Safe IoT approach  uhttps://doi.org/10.1109/ICCVE45908.2019.896505601145nas a2200421   4500000000100000000000100001008004100002260001300043100002700056700002400083700001700107700002000124700002200144700001700166700001400183700002100197700001900218700001600237700001800253700002200271700002100293700002300314700002100337700002000358700001700378700001500395700002400410700001700434700001800451700002000469700002200489700002200511700002200533700002200555700001900577245007800596856004900674       2019                            d  bSpringer1 aSotiris Diamantopoulos1 aDimitris Karamitros1 aLuigi Romano1 aLuigi Coppolino1 aVassilis Koutkias1 aKostas Votis1 aOana Stan1 aPaolo Campegiani1 aDavid Martinez1 aMarco Nalin1 aIlaria Baroni1 aFabrizio Clemente1 aGiuliana Faiella1 aCharis Mesaritakis1 aEvangelos Grivas1 aJanne Rasmussen1 aJan Petersen1 aIsaac Cano1 aElisa Puigdomènech1 aErol Gelenbe1 aJos Dumortier1 aMaja Voss-Knude1 aRaffaele Montella1 aAngelo Ciaramella1 aGiancarlo Fortino1 aAntonio Guerrieri1 aAntonio Liotta00aSecure Cross-Border Exchange of Health Related Data: The KONFIDO Approach  uhttps://doi.org/10.1007/978-3-030-34914-1_3000406nas a2200133   4500000000100000000000100001008004100002100001900043700002200062700001700084245011600101856003600217490001900253       2019                            d1 aKhaled Hussain1 aMohamed Bassyouni1 aErol Gelenbe00aAccurate and Energy-Efficient Classification with Spiking Random Neural Network: Corrected and Expanded Version  uhttp://arxiv.org/abs/1906.088640 vabs/1906.0886400322nas a2200121   4500000000100000000000100001008004100002100001300043700001700056245007200073856003600145490001900181       2019                            d1 aHuibo Bi1 aErol Gelenbe00aEmergency Management Systems and Algorithms: a Comprehensive Survey  uhttp://arxiv.org/abs/1907.041360 vabs/1907.0413600959nas a2200349   4500000000100000000000100001008004100002100002300043700002000066700002000086700002300106700002000129700002100149700001500170700001600185700002100201700002200222700001600244700002100260700001400281700001700295700001800312700001700330700002300347700001700370700002000387700002200407245011000429856004600539300001700585490000700602       2018                            d1 aPantelis Natsiavas1 aJanne Rasmussen1 aMaja Voss-Knude1 aKonstantinos Votis1 aLuigi Coppolino1 aPaolo Campegiani1 aIsaac Cano1 aDavid Marí1 aGiuliana Faiella1 aFabrizio Clemente1 aMarco Nalin1 aEvangelos Grivas1 aOana Stan1 aErol Gelenbe1 aJos Dumortier1 aJan Petersen1 aDimitrios Tzovaras1 aLuigi Romano1 aIoannis Komnios1 aVassilis Koutkias00aComprehensive user requirements engineering methodology for secure and interoperable health data exchange  uhttps://doi.org/10.1186/s12911-018-0664-0  a85:1–85:160 v1800437nas a2200181   4500000000100000000000100001008004100002100001100043700001900054700001700073700001100090700001500101700001300116245006200129856004500191300001200236490000700248       2018                            d1 aJun Du1 aChunxiao Jiang1 aErol Gelenbe1 aLei Xu1 aJianhua Li1 aYong Ren00aDistributed Data Privacy Preservation in IoT Applications  uhttps://doi.org/10.1109/MWC.2017.1800094  a68–760 v2500540nas a2200205   4500000000100000000000100001008004100002260001300043100001800056700001600074700002000090700001700110700001800127700002400145700001900169700002000188700002300208245005400231856004900285       2018                            d  bSpringer1 aIngrid Grenet1 aYonghua Yin1 aJean-Paul Comet1 aErol Gelenbe1 aVera Kurková1 aYannis Manolopoulos1 aBarbara Hammer1 aLazaros Iliadis1 aIlias Maglogiannis00aMachine Learning to Predict Toxicity of Compounds  uhttps://doi.org/10.1007/978-3-030-01418-6_3300331nas a2200121   4500000000100000000000100001008004100002260000900043100001700052700001900069245007500088856004600163       2018                            d  bIEEE1 aErol Gelenbe1 aYasin Kadioglu00aEnergy Life-Time of Wireless Nodes with Network Attacks and Mitigation  uhttps://doi.org/10.1109/ICCW.2018.840356100453nas a2200169   4500000000100000000000100001008004100002260000900043100001100052700001900063700001700082700001200099700001300111700001900124245009300143856004700236       2018                            d  bIEEE1 aJun Du1 aChunxiao Jiang1 aErol Gelenbe1 aZhu Han1 aYong Ren1 aMohsen Guizani00aNetworked Data Transaction in Mobile Networks: A Prediction-based Approach Using Auction  uhttps://doi.org/10.1109/IWCMC.2018.845053100444nas a2200169   4500000000100000000000100001008004100002260000900043100001100052700001700063700001900080700001200099700001300111700001900124245008400143856004700227       2018                            d  bIEEE1 aJun Du1 aErol Gelenbe1 aChunxiao Jiang1 aZhu Han1 aYong Ren1 aMohsen Guizani00aCognitive Data Allocation for Auction-based Data Transaction in Mobile Networks  uhttps://doi.org/10.1109/IWCMC.2018.845042801501nas a2200133   4500000000100000000000100001008004100002260001200043100001300055700001700068245008000085490001200165520119000177       2019                            d  c06/20191 aHuibo Bi1 aErol Gelenbe00aA Survey of Algorithms and Systems for Evacuating People in Confined Spaces0 v vol. 83 a<p>The frequency, destruction and costs of natural and human-made disasters in modern<br />
highly-populated societies have resulted in research on emergency evacuation and wayfinding, which<br />
has drawn considerable attention. The subject is now a multidisciplinary area of research where<br />
information and communication technologies (ICT), and in particular the Internet of Things (IoT),<br />
have a significant impact on sensing and computing dynamic reactions that mitigate or prevent<br />
the worst outcomes of disasters. This paper offers state-of-the-art knowledge in this area so as to<br />
share ongoing research results, identify the research gaps and address the need for future research.<br />
We present a comprehensive review of research on emergency evacuation and wayfinding, focusing<br />
on the algorithmic and system design aspects. Starting from the history of emergency management<br />
research, we identify the emerging challenges concerning system optimisation, evacuee behaviour<br />
optimisation and data analysis, and the additional energy consumption by ICT equipment that<br />
operates the emergency management infrastructure.</p>
00363nas a2200121   4500000000100000000000100001008004100002260003700043100001300080700001700093700001600110245011500126       2020                            d  c10/2020bIEEEXpressaNice France1 aHuibo Bi1 aErol Gelenbe1 aYanyan Chen00aIncentive mechanism for collective coordination in an urban intelligent transportation system using G-networks01186nas a2200157   4500000000100000000000100001008004100002260001200043100001700055700001700072700001600089245007400105490000800179520082700187022001401014       2020                            d  c04/20201 aWill Serrano1 aErol Gelenbe1 aYonghua Yin00aThe Random Neural Network with Deep Learning Clusters in Smart Search0 v3963 a<p>This paper proposes a Neurocomputing application that reorders the Web results obtained from different Web Search Engines emulating the way our brain takes decisions. The proposed application is based on the Random Neural Network with Deep Learning Clusters that evaluates and adapts Web result relevance by associating independently each Deep Learning Cluster to a specific Web Search Engine. In addition, this paper presents a Deep Learning Cluster to perform as a Management Cluster that decides the final result relevance based on the inputs from each independent Deep Learning cluster. The performance of the proposed Management Cluster is evaluated when included as an additional layer to the Deep Learning Clusters. On average; the proposed Deep Learning cluster structure improves Smart Search performance</p>
  a0925-231201976nas a2200145   4500000000100000000000100001008004100002260001200043100001700055700001700072245005900089490000800148520166000156022001401816       2020                            d  c07/20201 aWill Serrano1 aErol Gelenbe00aDeep Learning Clusters in the Cognitive Packet Network0 v3963 a<p>The Cognitive&nbsp;<a href="https://www.sciencedirect.com/topics/computer-science/packet-network" title="Learn more about Packet Network from ScienceDirect's AI-generated Topic Pages">Packet Network</a>&nbsp;(CPN) bases its routing decisions and flow control on the Random&nbsp;<a href="https://www.sciencedirect.com/topics/neuroscience/neural-networks" title="Learn more about Neural Networks from ScienceDirect's AI-generated Topic Pages">Neural Network</a>&nbsp;(RNN) Reinforcement&nbsp;<a href="https://www.sciencedirect.com/topics/computer-science/learning-algorithm" title="Learn more about Learning Algorithm from ScienceDirect's AI-generated Topic Pages">Learning algorithm</a>; this paper proposes the addition of a Deep Learning (DL) Cluster management structure to the CPN for Quality of Service metrics (Delay Loss and Bandwidth), Cyber Security keys (User, Packet and Node) and Management decisions (QoS, Cyber and CEO). The RNN already models how neurons transmit information using positive and negative impulsive signals whereas the proposed additional Deep Learning structure emulates the way the brain learns and takes decisions; this paper presents a brain model as the combination of both learning algorithms, RNN and DL. The proposed model has been simulated under different network sizes and scenarios and it has been validated against the CPN itself without DL clusters. The simulation results are promising; the presented CPN with DL clusters as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions.</p>
  a0925-231201780nas a2200181   4500000000100000000000100001008004100002260001200043100001100055700001700066700001900083700001200102700001300114245010300127490000700230520134700237022001401584       2020                            d  c05/20201 aJun Du1 aErol Gelenbe1 aChunxiao Jiang1 aZhu Han1 aYong Ren00aAuction-Based Data Transaction in Mobile Networks: Data Allocation Design and Performance Analysis0 v193 a<p>Mobile data traffic is experiencing unprecedented increases due to the proliferation of highly capable smartphones, laptops and tablets, and mobile data offloading can be used to move traffic from cellular networks to other wireless infrastructures such as small-cell base stations. This work addresses the related issue of data allocation, by proposing a novel infrastructure independent method based on the hotspot function of smartphones. In the proposed scheme, smartphones transfer data allowances among mobile users, so that users with excess data allowances act as accessible Wi-Fi hotspots, selling their data allowance to other users who need extra data allowances. To achieve this objective, we propose to use auctions with single and multiple data sellers. Efficient schemes based on auction models are discussed to sell the data allowances over successive days in a month, and over different time slots during a single day. Overall system performance is considered based on the behavior of mobile users, such as changing demands for the sale or purchase of data allowances. Together with the analytical results presented, our simulation experiments also indicate that knowledge of user behavior can significantly improve the performance of data allowance transactions, leading to highly efficient allocations among users.</p>
  a1558-066001493nas a2200145   4500000000100000000000100001008004100002100002300043700002000066700002200086700001700108245015800125490000600283520105800289       2021                            d1 aDionysios Kehagias1 aMarija Jankovic1 aMiltiadis Siavvas1 aErol Gelenbe00aInvestigating the Interaction between Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks0 v23 a<p>The Conference on Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks (EQSEM) was held as a virtual conference on October 20–21, 2020. This paper summarises the objectives and proceedings of this conference. It then briefly presents the keynotes and other papers which were presented. Then, in the context of the EU-funded research project SDK4ED which motivated this conference, we outline several solutions that are being developed for managing the potential inter-dependencies and corresponding trade-offs between design-time and runtime qualities in software applications, and review the key functionalities that have been implemented within the SDK4ED integrated platform as of this writing. Then, we briefly introduce four papers among those presented at the EQSEM Conference that are included in this issue of the journal SN Computer Science, presenting relevant research achievements of the SDK4ED Project on software maintainability, reliability, and energy efficiency.</p>
01829nas a2200121   4500000000100000000000100001008004100002100001700043700002200060245006300082490000700145520155500152       2021                            d1 aErol Gelenbe1 aMiltiadis Siavvas00aMinimizing Energy and Computation in Long-Running Software0 v113 a<p>Long-running software may operate on hardware platforms with limited energy resources<br />
such as batteries or photovoltaic, or on high-performance platforms that consume a large amount<br />
of energy. Since such systems may be subject to hardware failures, checkpointing is often used to<br />
assure the reliability of the application. Since checkpointing introduces additional computation time<br />
and energy consumption, we study how checkpoint intervals need to be selected so as to minimize a<br />
cost function that includes the execution time and the energy. Expressions for both the program’s<br />
energy consumption and execution time are derived as a function of the failure probability per<br />
instruction. A first principle based analysis yields the checkpoint interval that minimizes a linear<br />
combination of the average energy consumption and execution time of the program, in terms of the<br />
classical “Lambert function”. The sensitivity of the checkpoint to the importance attributed to energy<br />
consumption is also derived. The results are illustrated with numerical examples regarding programs<br />
of various lengths and showing the relation between the checkpoint interval that minimizes energy<br />
consumption and execution time, and the one that minimizes a weighted sum of the two. In addition,<br />
our results are applied to a popular software benchmark, and posted on a publicly accessible web<br />
site, together with the optimization software that we have developed.</p>
04403nas a2200241   4500000000100000000000100001008004100002260001200043100001700055700001700072700001800089700002200107700002300129700002000152700002200172700002000194700002000214245024800234856009900482490000700581520355900588022001404147       2021                            d  c04/20211 aErol Gelenbe1 aGuy Brasseur1 aLuc Cheffneux1 aVéronique Dehant1 aVéronique Halloin1 aJean-Paul Haton1 aMichel Judkiewicz1 aBernard Rentier1 aRomain Weikmans00aRéflexions sur la diffusion des connaissances a travers les grands colloques internationaux, les revues scientifiques, et la communication libre et rapide entre chercheurs et innovateurs dans un contexte de reduction de l'empreinte climatique  u{https://www.societe-informatique-de-france.fr/wp-content/uploads/2021/04/1024_17_2021_155.pdf0 v173 aAu début de l’année 2019, la classe Technologie et société de l’Académie royale de Belgique a suivi la proposition de son directeur d’orienter principalement ses travaux, durant deux ans, sur le thème de la Transition vers un futur désirable. Personne à ce moment n’imaginait que nous allions vivre une crise de l’ampleur de celle que nous traversons. La pandémie révèle le manque de résilience du monde dans lequel nous vivons ; elle en souligne particulièrement la complexité tout comme les interactions multiples qui lient les douze domaines que la classe avait souhaité aborder dans sa démarche. Si la santé est concernée au premier chef par la crise sanitaire, le monde de l’entreprise et du travail n’a pas manqué d’être directement impacté. Notre modèle économique et financier a été interpelé et la nécessité fut relevée de disposer d’infrastructures de bon niveau de même que d’une gouvernance capable de faire face à des situations critiques qui peuvent générer des risques touchant à la paix et à la sécurité. Il est aussi apparu combien les valeurs de solidarité, de civisme et de cohésion sociale retrouvent toute leur importance, tandis que la nécessité des connaissances scientifiques et de l’expertise est mise en exergue. Les technologies disruptives (en particulier le numérique) nous offrent la possibilité de garder le contact, tout en suscitant parfois de sérieux problèmes de respect de la vie privée et des droits humains. Notre système d’éducation s’en trouve lourdement perturbé et l’impact sur l’environnement et le climat est tangible tandis que la nature retrouve (un peu) ses droits (ressources naturelles). Or, dès avant l’apparition de la pandémie, un large consensus s’était dégagé au sein du monde politique et économique ainsi que dans la population des pays européens sur la nécessité de réagir énergiquement face à la crise climatique de plus en plus manifeste, qui menace l’humanité à l’horizon d’une génération. Aussi, a fortiori, le redémarrage de l’économie mondiale au lendemain d’un lock-down sanitaire ne peut-il faire l’impasse sur la prise en compte du changement climatique et de ses conséquences sociales, potentiellement beaucoup plus graves encore que celles causées par la pandémie elle-même. Face à un défi aussi colossal, les Académies ne peuvent se contenter de poursuivre quiètement leurs activités comme par le passé. La classe Technologie et société de l’Académie royale de Belgique, en particulier, se doit d’apporter une contribution, même modeste, à la réflexion générale nécessaire à l’éveil de la conscience des citoyens et dont nos dirigeants devraient pouvoir se nourrir afin de prendre les décisions qui s’imposent. Cette problématique, dont il serait naïf de ré- duire le niveau de grande complexité, est promue à un intérêt croissant, car au cœur d’un développement harmonieux des sociétés. Si l’innovation technologique ne peut seule prétendre résoudre les problèmes auxquels le monde est confronté, elle devrait en effet être en mesure d’apporter des réponses efficaces qui, sans cesse, seront pourtant confrontées à la question de la transparence des procédures de recherche et à la publicité des résultats. Dans ce débat, l’Union européenne et ses États membres, dans leurs champs d’action respectifs, auront à intervenir et à se choisir une ligne de conduite, responsable et soucieuse du bien commun.
  a2270-141901573nas a2200133   4500000000100000000000100001008004100002260003800043100001500081700001700096245009100113856003500204520120000239       2021                            d  bIEEEaNew Orleans, Louisiana, USA1 aMert Nakip1 aErol Gelenbe00aRandomization of Data Generation Times Improves Performance of Predictive IoT Networks  uhttps://wfiot2021.iot.ieee.org3 a<p>Input traffic from Internet of Things (IoT) devices is often both periodic and requires to be received by a given deadline. This can create congestion at instants of time when traffic flowing from multiple devices arrives at a shared input port or gateway, resulting in missed deadlines at the receiver. As a consequence, scheduling techniques such as the “Earliest Deadline First” (EDF) and “Priority based on Average Load” (PAL) are used to schedule the flow from different devices so as to try to satisfy the needs of the largest number of traffic flows in a timely fashion. In this paper, we propose the Randomization of flow Genaration Times (RGT) in order to smooth the total incoming traffic to the input port or gateway, on top of the use of EDF and PAL. We then evaluate the performance of RGT together with PAL and EDP, for traffic load with a varying number of up to 6400 IoT devices. Our simulation results show that RGT provides significantly better performance when added to EDF and PAL. Also, the additional computation required by RGT at each device can be quite small, suggesting that RGT is a very useful addition for improving the performance of IoT networks.</p>
00989nas a2200109   4500000000100000000000100001008004100002100001700043245007700060490000600137520073600143       2021                            d1 aErol Gelenbe00aRenforcer la place des universités européennes dans le concert mondial0 v23 a<p>Les pays de l’UE peuvent se targuer de posséder quelques-unes des plus prestigieuses universités et organismes de recherche dans le monde. Pourtant la part des innovations technologiques que produit le continent reste faible au regard du potentiel de recherches que l’UE représente. Comment redresser la barre et mieux armer les universités européennes dans la compétition exacerbée que se livrent les grandes puissances ? Après une analyse du financement et du mode de fonctionnement comparés des universités dans le monde occidental, l’auteur propose quelques recommandations fortes pour donner à la France et l’Europe de la recherche les moyens de se confronter à armes égales avec ses concurrents.</p>
01229nas a2200145   4500000000100000000000100001008004100002260000900043100001100052700001400063700001500077700001700092245009100109520088300200       2021                            d  bIEEE1 aNan Li1 aXiping Hu1 aEdith Ngai1 aErol Gelenbe00aCooperative Wireless Edges with Composite Resource Allocation in Hierarchical Networks3 a<p>With the expansion of the IoT, it is important to optimize available bandwidth to reliably support edge to device communications. Thus we propose a wireless network where each edge server communicates with its end devices using its wireless band as a primary channel, assisted by a secondary edge server that can relay communications via its own wireless band as a secondary channel. The network can optimize capacity by balancing load between primary and secondary wireless bands, and we analyze the geometry of achievable rate regions, depending on the state of bands modeled as Rayleigh fading channels. The allocation of a connection to the primary or secondary band is formulated as an optimization problem which is then solved, and illustrated with numerical examples. Index Terms—Edge computing, cooperation, non-orthogonal multiple access, resource allocation</p>
01621nas a2200193   4500000000100000000000100001008004100002260001500043100002000058700001700078700001800095700002200113700001800135700002000153245006800173856004500241490001000286520113100296       2021                            d  cApril 20211 aPiotr Fröhlich1 aErol Gelenbe1 aJerzy Fiołka1 aJacek Chęciński1 aMateusz Nowak1 aZdzisław Filus00aSmart SDN Management of Fog Services to Optimize QoS and Energy  uhttps://www.mdpi.com/1424-8220/21/9/31050 v21(9)3 aThe short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%.02819nas a2200253   4500000000100000000000100001008004100002260004300043100001800086700002100104700001900125700001600144700002100160700001700181700001900198700002100217700002400238700002500262700001800287700001600305245005000321300001200371520218200383       2021                            d  bALLEA - All European AcademiesaBerlin1 aDan Larhammar1 aMaria Baghramian1 aPiero Bianucci1 aGarvin Brod1 aDalibor Dobiáš1 aErol Gelenbe1 aRisto Kunelius1 aStefano Leonardi1 aStephan Lewandowsky1 aMariëtte Samenweten1 aAlison Powell1 aJane Suiter00aFact or Fake? Tackling Science Disinformation  apaper 53 a<p>The information landscape has undergone dramatic changes with the expansion of the internet and digital social media platforms. Information can be spread more rapidly and can reach more people than ever before. While this offers excellent possibilities to teach and educate and to disseminate information about research results and scientific evidence, it also comes with a downside: False information can be propagated with equal ease and speed. This discussion paper describes and discusses the problems and the consequences of science disinformation in three areas of concern, namely climate change, vaccines and pandemics, and what we can do to increase awareness and minimize harm caused by the spread of disinformation. It does so by highlighting the societal value of the scientific method, research integrity, open science communication and the resulting trust in science. The underlying question is how to protect the pillars of science from the severe consequences of disinformation while maintaining openness and democratic principles. This paper presents the central characteristics of science disinformation, its roots, its spread, and potential solutions. The mere existence of disinformation is hard to prevent in open societies with strong protection of individual rights and freedom of expression. The paper identifies underlying cognitive, social and economic mechanisms that amplify the spread of disinformation and evaluates potential solutions. Extensive research over the past several years has identified cognitive features of the human mind, as well as fast and efficient transmission channels, that contribute to the prevalence of science disinformation in our societies. Potential solutions cover a range of psychological, technical and political measures including inoculation, debunking, recommender systems, fact-checking, raising awareness, media literacy, and innovations in science communication and public engagement. Together, they contribute to tackling problems such as knowledge resistance, pseudoscience, undermining of trust, confirmation bias, filter bubbles, echo chambers, and other problems related to science disinformation.</p>
04152nam a2200169   4500000000100000000000100001008004100002260001300043100001600056700001700072700002500089700002000114700001800134245005300152490000600205520377100211       2020                            d  bSpringer1 aSabu Thampi1 aErol Gelenbe1 aMohammed Atiquzzaman1 aVipin Chaudhary1 aKuan-Ching Li00aAdvances in Computing and Network Communications0 v13 a<p>The Fourth International Conference on Computing and Network Communications (CoCoNet’20) provided a forum for sharing original research outcomes and practical development experiences among experts in the emerging areas of computing and communications. The conference was organized by Vellore Institute of Technology (VIT), Chennai, India. Due to the recent pandemic situation, the conference was conducted as a virtual event during October 14–17, 2020. The material was presented in a program that consisted of keynote talks, technical sessions, lightning talks, tutorials, symposiums, parallel sessions, workshops and hot off the press. CoCoNet’20 convened a well-tailored and handpicked collection of eminent speakers from renowned universities and industries in and outside India. The array of speakers included Dr. Ian T. Foster, Dr. Arumugam Nallanathan, Dr. Mohammed Atiquzzaman, Dr. Vipin Chaudhary, Dr. Dilip Krishnaswamy and Dr. Ljiljana Trajkovic. The conference received 181 submissions this year, out of which 98 papers (58 regular papers and 40 short papers) had been accepted. The papers were subjected to a rigorous review process that examined the significance, novelty and technical quality of the submission. The papers were presented in different sessions, namely the best paper sessions, regular paper sessions and short paper sessions. The proceedings of the conference is organized into two volumes. This volume is comprised of 51 papers, and the topical sections include Communications, Control and Signal Processing, Data Analytics and Networked Systems and Security. The success of the conference depends ultimately on the numerous people who have worked with us to plan and organize both the technical program and local arrangements. In particular, the wise advice and brilliant ideas of the program chairs, workshop and symposium chairs and industry track chairs in the organization of the technical program are gratefully appreciated. We would like to extend our heartfelt gratitude to the organizing committee and advisory committee members. The accomplishment of the motives of the conference is powered by the tireless efforts of many individuals. We would like to express our sincere gratitude to the TPC chairs, TPC members and additional reviewers who shared their technical expertise and assisted us in reviewing all the submitted papers. We would like to thank the general chairs, organizing committee members, steering committee, keynote speakers, session chairs and the conference attendees. We are thankful to&nbsp;all the authors for choosing this conference as a venue for presenting their research works. We express our wholehearted appreciation to the contributions of all those who apportioned their valuable time for the success of CoCoNet’20. We are grateful to Vellore Institute of Technology (VIT), Chennai, for organizing the conference. Recognition should go to the local organizing committee members who all have worked extremely hard for the details of important aspects of the conference programs. We appreciate the contributions of all the faculty and staff of VIT and the student volunteers who have selflessly contributed their time to make this virtual conference successful. We would like to express our gratitude to Senior Editor of Springer Nature, Aninda Bose, for his help and cooperation. We sincerely hope that CoCoNet’20 turned out to be a forum for excellent discussions that enabled new ideas to come about, promoting collaborative research. We are confident that the proceedings will serve as a momentous source of research references and knowledge, which will lead not only to the scientific and engineering findings but also to the development of new products and technologies.</p>
02264nas a2200205   4500000000100000000000100001008004100002100001700043700001700060700001700077700002200094700002300116700002000139700002200159700002000181700002000201245005900221490000900280520176900289       2021                            d1 aErol Gelenbe1 aGuy Brasseur1 aLuc Chefneux1 aVéronique Devant1 aVéronique Halloin1 aJean-Paul Haton1 aMichel Judkiewicz1 aBernard Rentier1 aRomain Weikmans00aOn	Sharing	Knowledge	And	Fostering	 “Open	Science”0 v20213 a<p>The crucial importance of science and technology and its accurate peer reviewed dissemination, has once again been demonstrated during the current pandemic. Thus the COVID-19 pandemic together with the inevitable energy transition required by climate change, lead us to consider the issue of scientific and technical communication, both for the written papers and proceedings that have largely moved online (but not always in open access), and the various types of seminars, workshops, and symposia that frequently involve air travel with substantial CO2 impact. Online meetings that have become recently very popular, as well as online repositories for publications, themselves have a significant CO2—as well as environmental—impact, due to the massive use of electricity by information and communication technologies (ICT) and of the environmentally unfriendly manufacturing processes and decommissioning of ICT equipment. Presented is a broad overview of these aspects, and some recommendations regarding the future organization of scientific and technical communication, including: (1) peer-reviewed journals and proceedings with online open access; (2) the importance of face to face seminars and symposia, together with online meetings, to maintain the serendipity and importance of direct human contact while reducing the need for air travel; (3) the peer evaluation of research and of academic and research staff and its dependence on publications and their qualitative—rather than excessively quantitative—evaluation, where the concept of impact should include the usefulness of research to education, industry and society; (4) and the crucial role of ICT in all these aspects and the questions raised by the sustainability of ICT itself.</p>
02365nas a2200145   4500000000100000000000100001008004100002100002200043700002300065700002300088700001700111245011500128490000700243520196900250       2021                            d1 aMiltiadis Siavvas1 aDionysios Kehagias1 aDimitrios Tzovaras1 aErol Gelenbe00aA hierarchical model for quantifying software security based on static analysis alerts and software metrics0 v293 a<p>Despite the acknowledged importance of quantitative security assessment in secure software development, current literature still lacks an efcient model for measuring internal software security risk. To this end, in this paper, we introduce a hierarchical security assessment model (SAM), able to assess the internal security level of software products based on low-level indicators, i.e., security-relevant static analysis alerts and software metrics. The model, following the guidelines of ISO/IEC 25010, and based on a set of thresholds and weights, systematically aggregates these low-level indicators in order to produce a high-level security score that refects the internal security level of the analyzed software. The proposed model is practical, since it is fully automated and operationalized in the form of a standalone tool and as part of a broader Computer-Aided Software Engineering (CASE) platform. In order to enhance its reliability, the thresholds of the model were calibrated based on a repository of 100 popular software applications retrieved from Maven Repository. Furthermore, its weights were elicited in a way to chiefy refect the knowledge expressed by the Common Weakness Enumeration (CWE), through a novel weights elicitation approach grounded on popular decision-making techniques. The proposed model was evaluated on a large repository of 150 open-source software applications retrieved from GitHub and 1200 classes retrieved from the OWASP Benchmark. The results of the experiments revealed the capacity of the proposed model to reliably assess internal security at both product level and class level of granularity, with sufcient discretion power. They also provide preliminary evidence for the ability of the model to be used as the basis for vulnerability prediction. To the best of our knowledge, this is the frst fully automated, operationalized and sufciently evaluated security assessment model in the modern literature</p>
01236nas a2200145   4500000000100000000000100001008004100002260000800043100002000051700001700071700001800088245005200106856005500158520087700213       2021                            d  bACM1 aPiotr Fröhlich1 aErol Gelenbe1 aMateusz Nowak00aReinforcement Learning and Energy-Aware Routing  uhttps://dl.acm.org/doi/pdf/10.1145/3472735.34733903 a<p>We present an approach that uses Reinforcement Learning (RL) with the Random Neural Network (RNN) acting as an adaptive critic, to route traffic in a SDN network, so as to minimize a composite Goal function that includes both packet delay and energy consumption per packet. We directly measure the traffic dependent energy consumption characeristics of the hardware that we use (including energy expended per packet) so as to parametrize the Goal function. The RL based algorithm with the RNN is implemented in a SDN controller that manages a multi-hop network which assigns service requests to specific servers so as to minimize the desired Goal. The overall system’s performance is evaluated through experimental measurements of packet delay and energy consumption under different traffic load values, demonstrating the effectiveness of the proposed approach.</p>
01068nas a2200121   4500000000100000000000100001008004100002260003500043100001500078700001700093245008400110520075200194       2021                            d  c7-11 Dec 2021bIEEEaBarcelona1 aMert Nakip1 aErol Gelenbe00aMIRAI Botnet Attack Detection with Auto-Associative Dense Random Neural Network3 aInternet connected IoT devices have often been particularly vulnerable to Botnet attacks of the Mirai family in recent years. Thus we develop an attack detection scheme for Mirai Botnets, using the Auto-Associative Dense Random Neural Network that has recently been successful for other attacks such as the SYN attack. The resulting method is trained with normal traffic and tested with attack traffic, and shown to result in high accuracy detection of attacks with low false alarms. The approach is compared on the same data set with two other common Machine learning methods (Lasso and KNN) and shown to have higher accuracy, and much lower computation times than KNN and slightly higher (but comparable) computation times with respect to Lasso.00581nas a2200109   4500000000100000000000100001008004100002100001700043245002900060490000700089520037500096       2021                            d1 aErol Gelenbe00aNOBEL PRIZE 50 YEARS AGO0 v523 a<p>As we recall the major contributions of Professor Dennis Gabor that resulted in his Nobel Prize in Physics in 1971 for the invention of holography, it is interesting to put his impact on science, technology, as well as humanity, in a broader context so as to better understand his experiences, and recognize the very significant role that he played in his time.</p>
01064nas a2200121   4500000000100000000000100001008004100002260002700043100001500070700001700085245006100102520077900163       2022                            d  bSpringeraNice, France1 aMert Nakip1 aErol Gelenbe00aBotnet Attack Detection with Incremental Online Learning3 a<p>In recent years, IoT devices have often been the target of Mirai Botnet attacks. This paper develops an intrusion detection method based on Auto-Associated Dense Random Neural Network with incremental online learning, targeting the detection of Mirai Botnet attacks. The proposed method is trained only on benign IoT traffic while the IoT network is online; therefore, it does not require any data collection on benign or attack traffic. Experimental results on a publicly available dataset have shown that the performance of this method is considerably high and very close to that of the same neural network model with offline training. In addition, both the training and execution times of the proposed method are highly acceptable for real-time attack detection.</p>
01547nas a2200193   4500000000100000000000100001008004100002260003900043100002200082700001700104700002400121700002400145700002200169700001500191700002300206700002300229245009800252520100300350       2022                            d  bIEEEaVilanova i la Geltrú, Spain1 aMiltiadis Siavvas1 aErol Gelenbe1 aDimitrios Tsoukalas1 aIlias Kalouptsoglou1 aMaria Mathioudaki1 aMert Nakip1 aDionysios Kehagias1 aDimitrios Tzovaras00aThe IoTAC Software Security-by-Design Platform: Concept, Challenges, and Preliminary Overview3 a<p>Critical everyday activities handled by modern IoT Systems imply that security is of major concern both for the end-users and the industry. Securing the IoT System Architecture is commonly used to strengthen its resilience to malicious attacks. However, the security of software running on the IoT must be considered as well, since the exploitation of its vulnerabilities can infringe the security of the overall system, regardless of how secure its architecture may be. Thus, we present an IoT Software Security-by-Design (SSD) Platform, which provides mechanisms for monitoring and optimizing the security of IoT software applications throughout their development lifecycle, to validate the broader security of the IoT software. This paper describes the proposed SSD platform that leverages security information from all phases of development, using some novel mechanisms that have been implemented, and which can lead to a holistic security evaluation and future security certification.</p>
01295nas a2200121   4500000000100000000000100001008004100002260002300043100001700066700001500083245005800098520101700156       2022                            d  bIEEEaNice, France1 aErol Gelenbe1 aMert Nakip00aG-Networks Can Detect Different Types of Cyberattacks3 a<p>Malicious network attacks are a serious source of concern, and machine learning techniques are widely used to build Attack Detectors with off-line training with real attack and non-attack data, and used online to monitor system entry points connected to networks. Many machine learning based Attack Detectors are typically trained to identify specific types attacks, and the training of such algorithms to cover several types of attacks may be excessively time consuming. This paper shows that G-Networks, which are queueing networks with product form solution and special customers such as negative customers and triggers, can be trained just with “non-attack” traffic, can accurately detect several different attack types. This is established with a special case of G-Networks with triggerred customer movement. A DARPA attack and non-attack traffic repository is used to train and test the the G-Network, yielding comparable or clearly better accuracy than most known attack detection techniques.</p>
01397nas a2200133   4500000000100000000000100001008004100002260012600043100001700169700001600186245005500202520098400257020002201241       2022                            d  c08/2022bIEEE Xplore, CC 2022 - IEEE International Conference on Communications, 2022, pp. 2732-2737aSeoul, South Korea 1 aErol Gelenbe1 aKarl Sigman00aIoT Traffic Shaping and the Massive Access Problem3 a<p>IoT gateways aim to meet the deadlines and QoS&nbsp;needs of packets from as many IoT devices as possible, though this can lead to a form of congestion known as the Massive&nbsp;Access Problem (MAP). While much work was conducted on&nbsp;predictive or reactive scheduling schemes to match the arrival&nbsp;process of packets to the service capabilities of IoT gateways, such&nbsp;schemes may use substantial computation and communication&nbsp;between gateways and IoT devices. This paper proves that&nbsp;the recently proposed “Quasi-Deterministic-Transmission-Policy&nbsp;(QDTP)” traffic shaping approach which delays packets at IoT&nbsp;devices, substantially alleviates the MAP: QDTP does not increase&nbsp;overall end-to-end delay and reduces gateway queue length. We&nbsp;then introduce the Adaptive Non-Deterministic Transmission Policy (ANTP) that requires only one packet buffer at the gateway,&nbsp;offering substantial QoS improvement over FIFO scheduling.</p>
  a978-1-5386-8347-700269nas a2200109   4500000000100000000000100001008004100002260001400043100001300057700001700070245007200087       2022                            d  bIOS Press1 aHuibo Bi1 aErol Gelenbe00aSearching and Rescuing Victims in Emergency: A Comprehensive Survey00385nas a2200145   4500000000100000000000100001008004100002260002000043100001700063700002000080700002300100700001600123700001800139245008200157       2022                            d  bSpringer Nature1 aErol Gelenbe1 aMarija Jankovic1 aDionysios Kehagias1 aAnna Marton1 aAndras Vilmos00aSecurity in Computer and Information Sciences: Second International Symposium00345nas a2200145   4500000000100000000000100001008004100002260001300043100001700056700001800073700002000091700001700111700002200128245004900150       2022                            d  bSpringer1 aErol Gelenbe1 aMateusz Nowak1 aPiotr Fröhlich1 aJerzy Fiolka1 aJacek Chęciński00aEnergy, QoS and Security Aware Edge Services00635nas a2200205   4500000000100000000000100001008004100002100001700043700001700060700001800077700002200095700001900117700002300136700002200159700001800181700002100199245007900220856010600299022002400405       2022                            d1 aErol Gelenbe1 aGuy Brasseur1 aLuc Cheffneux1 aVéronique Dehant1 aAnna Fabjanska1 aVéronique Halloin1 aMichel Judkiewicz1 aVladimir Mrsa1 aIgnacio Llorente00aChallenges for European Science and Technology Driven Innovation in Europe  uhttps://www.euro-case.org/challenges-for-european-science-and-technology-driven-innovation-in-europe/  aReport to EURO-CASE01121nas a2200157   4500000000100000000000100001008004100002260001300043100001700056700001800073700002000091700001700111700002200128245004900150520076400199       2022                            d  bSpringer1 aErol Gelenbe1 aMateusz Nowak1 aPiotr Fröhlich1 aJerzy Fiolka1 aJacek Chęciński00aEnergy, QoS and Security Aware Edge Services3 a<p>With the development of communication technologies and the increasing bandwidth of optical fibres and transmission speeds in current 5G and future 6G wireless networks, there is a growing demand for solutions organising traffic in such networks, taking into account both end-to-end transmissions and the possibility of data processing by edge services. The most pressing problems of today’s computer networks are not only bandwidth and transmission delays, but also security and energy consumption, which is becoming increasingly important in today’s climate. This paper presents a solution based on neural networks that organises network traffic taking into account the above criteria - quality of service (QoS), energy consumption and security.</p>
00609nas a2200109   4500000000100000000000100001008004100002100001700043245002900060490000700089520040300096       2022                            d1 aErol Gelenbe00aNOBEL PRIZE 50 YEARS AGO0 v523 a<p>As we recall the major contributions of Professor Dennis Gabor that resulted in his<br />
Nobel Prize in Physics in 1971 for the invention of holography, it is interesting to put<br />
his impact on science, technology, as well as humanity, in a broader context so as<br />
to better understand his experiences, and recognize the very significant role that he<br />
played in his time.</p>
01157nas a2200169   4500000000100000000000100001008004100002100001700043700001600060700001400076700001200090700001700102700001600119700001400135245006300149520077500212       2022                            d1 aErol Gelenbe1 aPaul Pearce1 aV. Kaptan1 aYu Wang1 aN.S Walmsley1 aP. Gardiner1 aJ. Moffat00aA Dynamic Model for Identifying Enemy Collective Behaviour3 a<p>Recent advances in Command and Control (C2) modelling have developed<br />
algorithmic representations of the command decision making process at both<br />
the tactical (Rapid Planning) and operational (Deliberate Planning) levels of<br />
command. In this paper, the development of methods by which indicators of<br />
enemy/group behaviour can be extracted is discussed. The aim of this work<br />
is to use the grouping techniques to establish, with more certainty, force size<br />
and direction. We describe a multi-agent model approach, based on a<br />
hierarchical framework, suitable for identifying indicators of collaborative<br />
behaviour associated with enemy agile mission groups that commonly feature<br />
in the modern Networked Era.</p>
02105nas a2200145   4500000000100000000000100001008004100002100001900043700001700062700001900079700001700098700001700115245007200132520175500204       2022                            d1 aDebanjan Konar1 aErol Gelenbe1 aSoham Bhandary1 aAditya Sarma1 aAttila Cangi00aRandom Quantum Neural Networks (RQNN) for Noisy Image Recog- nition3 a<p>Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision<br />
making, signal processing, and image recognition tasks. However, their implementation has been<br />
limited to deterministic digital systems that output probability distributions in lieu of stochastic be-<br />
haviors of random spiking signals. We introduce the novel class of supervised Random Quantum<br />
Neural Networks (RQNNs) with a robust training strategy to better exploit the random nature of<br />
the spiking RNN. The proposed RQNN employs hybrid classical-quantum algorithms with super-<br />
position state and amplitude encoding features, inspired by quantum information theory and the<br />
brain’s spatial-temporal stochastic spiking property of neuron information encoding. We have ex-<br />
tensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via<br />
the PennyLane Quantum simulator with a limited number of qubits. Experiments on the MNIST,<br />
FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an av-<br />
erage classification accuracy of 94.9%. Additionally, the experimental findings illustrate the proposed<br />
RQNN’s effectiveness and resilience in noisy settings, with enhanced image classification accuracy<br />
when compared to the classical counterparts (RNNs), classical Spiking Neural Networks (SNNs), and<br />
the classical convolutional neural network (AlexNet). Furthermore, the RQNN can deal with noise,<br />
which is useful for various applications, including computer vision in NISQ devices. The PyTorch<br />
code 1 is made available on GitHub to reproduce the results reported in this manuscript.</p>
00360nas a2200145   4500000000100000000000100001008004100002260002700043100001700070700002000087700002300107700001600130700001800146245005000164       2022                            d  bSpringeraNice, France1 aErol Gelenbe1 aJankovic Marija1 aKehagias Dionysios1 aMarton Anna1 aVilmos Andras00aSecurity in Computer and Information Sciences01621nas a2200145   4500000000100000000000100001008004100002260001200043100001700055700001500072245009600087856004900183490000700232520123600239       2022                            d  c10/20221 aErol Gelenbe1 aMert Nakip00aTraffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices  uhttps://ieeexplore.ieee.org/document/99695940 v103 a<p>A novel online Compromised Device Identification System (CDIS) is presented to identify IoT devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources and destinations that transmit packets. The method uses specific metrics that are selected for this purpose and which are easily extracted from network traffic, and trains itself online during normal operation with an Auto-Associative Dense Random Neural Network (AADRNN) using traffic metrics measured as traffic arrives. As it operates, the AADRNN is trained with auto-associative learning only using traffic that it estimates as being benign, without prior collection of different attack data. The experimental evaluation on publicly available Mirai Botnet attack data shows that CDIS achieves high performance with Balanced Accuracy of 97%, despite its low on-line training and execution time. Experimental comparisons show that the AADRNN with sequential (online) auto-associative learning, provides the best performance among six different state-of-the-art machine learning models. Thus CDIS can provide crucial effective information to prevent the spread of Botnet attacks in IoT networks having multiple devices and IP addresses.</p>
01179nas a2200133   4500000000100000000000100001008004100002260003300043100001700076700001500093245006900108856005000177520081800227       2023                            d  bIEEEaLondon, United Kingdom1 aErol Gelenbe1 aMert Nakip00aReal-Time Cyberattack Detection with Offline and Online Learning  uhttps://ieeexplore.ieee.org/document/101898123 a<p>This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network, which were developed in the HORIZON 2020 IoTAC Project. Some of these algorithms require offline learning, while others require the algorithm to learn during its normal operation while it is also testing the flow of incoming traffic to detect possible attacks. Most of the methods we present are designed to be used at a single node, while one specific method collects data from multiple network ports to detect and monitor the spread of a Botnet. The evaluation of the accuracy of all the methods is carried out with real attack traces. These novel methods are also compared with other state-of-the-art approaches, showing that they offer better or equal performance,</p>
01089nas a2200145   4500000000100000000000100001008004100002260003800043100002400081700001500105700001700120245008300137856005000220520067300270       2023                            d  bIEEEaDaytona Beach, Florida, USA1 aMohammed Nasereddin1 aMert Nakip1 aErol Gelenbe00aMeasurement Based Evaluation and Mitigation of Flood Attacks on a LAN Test-Bed  uhttps://ieeexplore.ieee.org/document/102233583 a<p>The IoT is vulnerable to network attacks, and Intrusion Detection Systems (IDS) can provide high attack detection accuracy and are easily installed in IoT Servers. However, IDS are seldom evaluated in operational conditions which are seriously impaired by attack overload. Thus a Local Area Network testbed is used to evaluate the impact of UDP Flood Attacks on an IoT Server, whose first line of defence is an accurate IDS. We show that attacks overload the multi-core Server and paralyze its IDS. Thus a mitigation scheme that detects attacks rapidly, and drops packets within milli-seconds after the attack begins, is proposed and experimentally evaluated.</p>
01516nas a2200133   4500000000100000000000100001008004100002260002100043100001700064700002400081245009800105856005900203520112000262       2023                            d  bIEEEaExeter, UK1 aErol Gelenbe1 aMohammed Nasereddin00aProtecting IoT Servers Against Flood Attacks with the Quasi Deterministic Transmission Policy  uhttps://ieeexplore.ieee.org/abstract/document/105389613 a<p>Servers at Supply Chains that receive packets from IoT devices should meet the QoS needs of incoming packets, and protect the system from Cyberattacks. UDP Floods are often part of Cyberattacks that overwhelm Supply Chains and the IoT through congestion that paralyzes their operation and limits their ability for timely Attack Detection.Thus this paper proposes an architecture that protects a connected Server using a Smart Quasi-Deterministic Transmission Policy Forwarder placed at its input port. This Forwarder shapes the incoming traffic, sends it to the Server without modifying the overall packet delay, and avoids Server congestion. The relevant theoretical background is reviewed, and measurements during a UDP Flood Attack are provided to compare the Server performance, with and without the Forwarder. It is seen that during a UDP Flood Attack, the Forwarder protects the Server from congestion allowing it to effectively identify Attack Packets. The resulting Forwarder congestion is also eliminated with “drop” commands generated by the Forwarder, or sent by the Server to the Forwarder.</p>
01410nas a2200133   4500000000100000000000100001008004100002260000900043100001500052700001500067700001700082245009000099520108700189       2024                            d  bIEEE1 aMert Nakip1 aBaran Gül1 aErol Gelenbe00aDecentralized Online Federated G-Network Learning for Lightweight Intrusion Detection3 a<p>Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. While Machine Learning (ML)-based Intrusion Detection Systems (IDSs) have been shown to be extremely promising in detecting these attacks, the need to learn large amounts of labeled data often limits the applicability of ML-based IDSs to cybersystems that only have access to private local data. To address this issue, this paper proposes a novel Decentralized and Online Federated Learning Intrusion Detection (DOF-ID) architecture. DOF-ID is a collaborative learning system that allows each IDS used for a cybersystem to learn from experience gained in other cybersystems in addition to its own local data without violating the data privacy of other systems. As the performance evaluation results using public Kitsune and Bot-IoT datasets show, DOF-ID significantly improves the intrusion detection performance in all collaborating nodes simultaneously with acceptable computation time for online learning.</p>
02474nas a2200157   4500000000100000000000100001008004100002260001300043100001500056700001700071245007300088856005900161490000700220520207500227022001402302       2024                            d  c05/2024 1 aMert Nakip1 aErol Gelenbe00aOnline Self-Supervised Deep Learning for Intrusion Detection Systems  uhttps://ieeexplore.ieee.org/abstract/document/105312670 v193 a<p>This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online {Deep Learning (DL)} based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known {machine learning and deep learning} models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.</p>

<p>This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known machine learning and deep learning models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.</p>
  a1556-601301732nas a2200109   4500000000100000000000100001008004100002100001700043700001500060245008300075520146400158       2023                            d1 aErol Gelenbe1 aMert Nakip00aIoT Network Cybersecurity Assessment with the Associated Random Neural Network3 a<p>This paper proposes a method to assess the security of an n device, or IP address, IoT network by simultaneously identifying all the compromised IoT devices and IP addresses. It uses a specific Random Neural Network (RNN) architecture composed of two mutually interconnected sub-networks that complement each other in a recurrent structure, called the Associated RNN (ARNN). For each of the n devices or IP addresses in the IoT network, two distinct neurons of the ARNN advocate opposite views: compromised or not compromised. The fully interconnected 2 n neuron ARNN structure of paired neurons learns offline from ground truth data. Thus rather than requiring a separate attack detector at each network node, the ARNN offers a single overall attack detector that observes the incoming traffic at each node, learns about the interdependencies between network nodes, and formulates a recommendation for each device or IP address in an IoT network. The ARNN weight initialization and learning algorithm are discussed, and the ARNN performance is evaluated using real attack data, and compared against several learning and testing techniques. Results are obtained both for off-line learning with ground truth data, and for on-line incremental learning using a simplified average metric measured from incoming packet traffic. Comparisons with the best state-of-the-art techniques show that the ARNN significantly outperforms previously known approaches.</p>
01516nas a2200133   4500000000100000000000100001008004100002260001200043100001700055245006800072490000900140520121900149022001401368       2023                            d  c08/20231 aErol Gelenbe00aElectricity Consumption by ICT: Facts, trends, and measurements0 v20233 a<p>This paper considers key issues surrounding energy consumption by information and communication technologies (ICT), which has been steadily growing and is now attaining approximately 10% of the worldwide electricity consumption with a significant impact on greenhouse gas emissions. The perimeter of ICT systems is discussed, and the role of the subsystems that compose ICT is considered. Data from recent years is used to understand how each of these sub-systems contribute to ICT's energy consumption. The quantitatively demonstrated positive correlation between the penetration of ICT in the world's different economies and the same economies' contributions to undesirable greenhouse gas emissions is also discussed. The paper also examines how emerging technologies such as 5G, AI, edge computing, and cryptocurrencies are contributing to the worldwide increase in electricity consumption by ICT, despite the increases in ICT efficiency, in terms of energy consumed per bit processed, stored, or transmitted. The measurement of specific ICT systems' electricity consumption is also addressed, and the manner in which this consumption can be minimized in a specific edge computing context is discussed.</p>
  a1530-218001745nas a2200145   4500000000100000000000100001008004100002260001200043100001700055700001500072700002200087245005800109490000800167520142400175       2024                            d  c10/20241 aErol Gelenbe1 aMert Nakip1 aMiltiadis Siavvas00aSystem-wide vulnerability of multi-component software0 v1963 a<p>In software systems comprised of many interconnected components, the vulnerability of each component will affect the vulnerability of other components and of the system as a whole. Existing techniques allow the quantification of the vulnerability of individual components taken singly, but the assessment of their vulnerability when they are interconnected or interdependent remains a challenge. The present work addresses this problem with a novel System-Wide Vulnerability Assessment (SWVA) framework for interconnected software components, based on an Associated Random Neural Network (ARNN) that estimates the system-wide vulnerability of all software components from known local vulnerabilities of individual components, and from their interconnections. The ARNN uses a problem-specific weight initialization, and learns from existing software system examples with a gradient-based deep learning algorithm. The ARNN is then used to assess the vulnerability of hitherto unseen software systems. The performance of the proposed ARNN-based SWVA framework is evaluated and compared against several well-known machine learning techniques on 13 different versions of a real-world software system with up to 11 components. The experimental results show the superior performance of the ARNN achieving above 85% median accuracy and good high scalability with respect to the number of connected software components.</p>
02510nas a2200145   4500000000100000000000100001008004100002260001200043100001700055700001500072700001500087245016100102490000700263520209400270       2024                            d  c12/20241 aErol Gelenbe1 aBaran Gül1 aMert Nakip00aDISFIDA: Distributed Self-Supervised Federated Intrusion Detection Algorithm with Online Learning for Health Internet of Things and Internet of Vehicles  0 v283 a<p>Networked health systems are often the victims of cyberattacks with serious consequences for patients and healthcare costs, with the Internet of Things (IoT) being an additional prime target. In future systems we can imagine that the Internet of Vehicles (IoV) will also be used for conveying patients for diagnosis and treatment in an integrated manner. Thus the medical field poses very significant and specific challenges since even for a single patient, several providers may carry out tests or offer healthcare services, and may have distinct interconnected sub-contractors for services such as ambulances and connected cars, connected devices or temporary staff providers, that have distinct confidentiality requirements on top of possible commercial competition. On the other hand, these distinct entities can be subject to similar or coordinated attacks, and could benefit from each others’ cybersecurity experience to better detect and mitigate cyberattacks. Thus the present work proposes a novel Distributed Self-Supervised Federated Intrusion Detection Algorithm (DISFIDA), with Online Self-Supervised Federated Learning, that uses Dense Random Neural Networks (DRNN). In DISFIDA learning data is private, and neuronal weights are shared among Federated partners. Each partner in DISFIDA combines its synaptic weights with those it receives other partners, with a preference for those weights that have closer numerical values to its own weights which it has learned on its own. DISFIDA is tested with three open-access datasets against five benchmark methods, for two relevant IoT healthcare applications: networks of devices (e.g., body sensors), and Connected Smart Vehicles (e.g., ambulances that transport patients). These tests show that the DISFIDA approach offers 100% True Positive Rate for attacks (one percentage point better than comparable state of the art methods which attain 99%) so that it does better at detecting attacks, with 99% True Negative Rate similar to state-of-the-art Federated Learning, for Distributed Denial of Service (DDoS) attacks.</p>
01127nas a2200145   4500000000100000000000100001008004100002260002500043100002400068700001500092700001700107245006800124856005900192520073000251       2024                            d  bIEEEaKrakow, Poland1 aMohammed Nasereddin1 aMert Nakip1 aErol Gelenbe00aDeep Learning Intrusion Detection and Mitigation of DoS Attacks  uhttps://ieeexplore.ieee.org/abstract/document/107865603 a<p>Internet of Things (IoT) networks are highly vulnerable to common network DoS and DDoS attacks, which flood limited system resources or IoT devices, overwhelming them with large numbers of attack packets. In order to mitigate such attacks, this paper develops a lightweight yet effective Intrusion Detection and Prevention System (IDPS), that sequentially detects and mitigates the attack via a Deep Random Neural Network (DRNN) and a Drop-Idle-Repeat process. The IDPS is evaluated for UDP Floods, attacks on an experimental test-bed. The results show that UDP Flood attacks can be mitigated with the proposed IDPS, allowing the system to continue routine operations, and resume communications when the attack ends.</p>
01641nas a2200145   4500000000100000000000100001008004100002100001800043700001400061700001400075700001700089245004400106856011400150520123100264       2024                            d1 aGuoliang Zhou1 aYijia Liu1 aZheng Yan1 aErol Gelenbe00aIs ChatGPT Trustworthy Enough? A Review  uhttps://www.researchgate.net/publication/385065063_Is_ChatGPT_Trustworthy_Enough_A_Review#fullTextFileContent3 a<p>ChatGPT, as an advanced model that seamlessly integrates into diverse digital interactions,<br />
shows great potential to enhance the performance of consumer technology and reshape its<br />
landscape. The critical question of its trustworthiness and associated challenges becomes<br />
increasingly prominent. However, the literature still lacks a thorough review to study its trust. This<br />
comprehensive review explores whether ChatGPT is trustworthy enough by navigating the<br />
multifaceted realm of large language models, placing a significant focus on its exemplar model,<br />
ChatGPT. Our exploration traverses the complex interplay of factors impacting user trust, including<br />
trust in ChatGPT itself and trust in its utilization and dissemination. By delving into insightful<br />
perspectives on the ChatGPT trustworthiness regarding a set of evaluation criteria including<br />
fundamental properties, subjective properties, security and privacy, we find that ChatGPT is far from<br />
trustworthy based on related literature review. This paper sheds light on the weakness of ChatGPT<br />
trust and provides the trajectory of future development in the realm of large language models</p>
02359nas a2200145   4500000000100000000000100001008004100002260001500043100002000058700001700078700001600095245007400111856014500185520188300330       2024                            d  bIEEEXplore1 aJacob Bergquist1 aErol Gelenbe1 aKarl Sigman00aOn an Adaptive-Quasi-Deterministic Transmission Policy Queueing Model  uhttps://www.researchgate.net/publication/384288968_On_an_Adaptive-Quasi-Deterministic_Transmission_Policy_Queueing_Model#fullTextFileContent3 a<p>We analyze, further and deeper, a recently proposed<br />
technique for addressing the Massive Access Problem (MAP), an<br />
issue in telecommunications which arises when too many devices<br />
transmit packets to a gateway in quick succession. This technique,<br />
the Adaptive-Quasi-Deterministic Transmission Policy (AQDTP)<br />
is a special case of “traffic shaping” which involves delaying<br />
some packets at the points of origin to alleviate congestion at<br />
the routers. One nice feature of AQDTP is that it loses no<br />
packets and allows an infinite buffer. In this work, to clarify<br />
the approach in a general queueing theory framework, and to<br />
move beyond the original telecommunications application, we<br />
frame these potential delays as time spent at a caf´e by customers<br />
before proceeding to a service facility. We first present some<br />
sample-path results that significantly refine and expand upon<br />
what was shown in previous work, and then present further<br />
results under a general stationary ergodic stochastic framework.<br />
In the sample-path realm, we give conditions that ensure AQDTP<br />
will not change the total delay and sojourn time of any customer<br />
as compared to what that customer would have experienced if<br />
there was no caf´e; but we also prove that AQDTP can never<br />
reduce the total delay. The difference is that, under AQDTP, some<br />
of that delay is spent at the caf´e instead of in the queue/line at the<br />
service facility. In a stochastic framework, our focus is on stability<br />
and constructing proper stationary versions of the model. Under<br />
i.i.d. assumptions we dig deeper by proving Harris recurrence<br />
of an underlying two-dimensional Markov process, and explicitly<br />
find positive recurrent regeneration points.</p>
02352nas a2200157   4500000000100000000000100001008004100002260001200043100001400055700001700069700001000086245009200096856003900188490000700227520196000234       2024                            d  c03/20241 aYuting Ma1 aErol Gelenbe1 aK Liu00aImpact of IoT system imperfections and passenger errors on cruise ship evacuation delay  u https://doi.org/10.3390/s240618500 v243 a<p>Cruise ships and other naval vessels include automated Internet of Things (IoT)-based evacuation systems for the passengers and crew to assist them in case of emergencies and accidents. The technical challenges of assisting passengers and crew to safety during emergencies include various aspects such as sensor failures, imperfections in the sound or display systems that are used to direct evacuees, the timely selection of optimum evacuation routes for the evacuees, as well as computation and communication delays that may occur in the IoT infrastructure due to intense activities during an emergency. In addition, during an emergency, the evacuees may be confused or in a panic, and may make mistakes in following the directions offered by the evacuation system. Therefore, the purpose of this work is to analyze the effect of two important aspects that can have an adverse effect on the passengers’ evacuation time, namely (a) the computer processing and communication delays, and (b) the errors that may be made by the evacuees in following instructions. The approach we take uses simulation with a representative existing cruise ship model, which dynamically computes the best exit paths for each passenger, with a deadline-driven Adaptive Navigation Strategy (ANS). Our simulation results reveal that delays in the evacuees’ reception of instructions can significantly increase the total time needed for passenger evacuation. In contrast, we observe that passenger behavior errors also affect the evacuation duration, but with less effect on the total time needed to evacuate passengers. These findings demonstrate the importance of the design of passenger evacuation systems in a way that takes into account all realistic features of the ship’s indoor evacuation environment, including the importance of having high-performance data processing and communication systems that will not result in congestion and communication delays.</p>
00304nas a2200121   4500000000100000000000100001008004100002260003800043100001400081700001700095700001600112245005400128       2024                            d  c11/2024bIEEEaOttawa, ON, Canada1 aYuting Ma1 aErol Gelenbe1 aKezhong Liu00aIoT Performance for Maritime Passenger Evacuation01596nas a2200193   4500000000100000000000100001008004100002260001200043100001800055700001500073700001400088700001100102700001900113700001700132700001700149245009100166490000800257520113700265       2024                            d  c12/20241 aMinqiang Yang1 aEdith Ngai1 aXiping Hu1 aBin Hu1 aJiangchuan Liu1 aErol Gelenbe1 aVictor Leung00aDigital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection0 v1123 a<p>Smartphones are widely used as portable data collectors for wearable and healthcare sensors that can passively collect data streams related to the environment, health status, and behaviors. Recent research shows that the collected data can be used to monitor not only the physical states but also the mental health of individuals. However, extracting the features of digital phenotypes that characterize major depressive disorder (MDD) is technically challenging and may raise significant privacy concerns. Addressing such challenges has become the focus of many researchers. This article provides a comprehensive analysis of several key issues related to ubiquitous sensing to aid in detecting MDD. Specifically, this article analyzes existing methodologies and feature extraction algorithms used to detect possible MDD through digital phenotyping from smartphone data. In particular, five types of features are summarized and explained, namely, location, movement, rhythm, sleep, and social and device usage. Finally, related limitations and challenges are discussed to provide paths for further research and engineering.</p>
02029nas a2200133   4500000000100000000000100001008004100002260001200043100001700055245005500072856004400127490000700171520171700178       2025                            d  c02/20251 aErol Gelenbe00aMinimizing delay and power consumption at the edge  uhttps://www.mdpi.com/1424-8220/25/2/5020 v253 a<p>Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. These prior approaches have two shortcomings: (a) there is no guarantee that optimum solutions are achieved, and (b) they do not provide an explicit formula for the fraction of tasks that are allocated to the different servers to achieve a specified optimum. This paper offers a radically different and mathematically based principled method that explicitly computes the optimum fraction of jobs that should be allocated to the different servers to (1) minimize the average latency (delay) of the jobs that are allocated to the edge servers and (2) minimize the average energy consumption of these jobs at the set of edge servers. These results are obtained with a mathematical model of a multiple-server edge system that is managed by a task distribution platform, whose equations are derived and solved using methods from stochastic processes. This approach has low computational cost and provides simple linear complexity formulas to compute the fraction of tasks that should be assigned to the different servers to achieve minimum latency and minimum energy consumption.</p>
01576nas a2200157   4500000000100000000000100001008004100002260001200043100001700055700001500072700002200087245008900109856007600198490000700274520113700281       2024                            d  c09/20241 aErol Gelenbe1 aMert Nakip1 aMiltiadis Siavvas00aSystem Wide Vulnerability and Trust in Multi-Component Communication System Software  uhttps://drive.google.com/file/d/1123-TES0XmyG8_l1XlmAfdWV5Jf6QPeo/view 0 v393 a<p>In the software rich environment of 6G,&nbsp;systems will be surrounded by edge devices that support distributed software&nbsp;systems which are critical to&nbsp;operations. Such systems may also be subject to frequent&nbsp;updates or uploads of individual software components.&nbsp;Trust in such systems will therefore depend on our ability&nbsp;to rapidly ensure that such software is not vulnerable to&nbsp;cyberattacks or malicious compromises. Thus this paper&nbsp;presents a novel System-Wide Vulnerability Assessment&nbsp;(SWVA) framework based on Machine Learning, that&nbsp;can be frequently activated to assess the vulnerability of&nbsp;interconnected software components over edge systems.&nbsp;The performance of the SWVA framework is illustrated&nbsp;by assessing the vulnerability of 13 versions of a real world 11 component software system, and comparingthe ARNN results against the well-known ML models&nbsp;MLP, KNN, and Lasso. The results show the superior&nbsp;performance of SWVA, offering over 85% median accuracy&nbsp;and good scalability as the number of connected software&nbsp;components increases.</p>
02038nas a2200145   4500000000100000000000100001008004100002260001200043100001700055700002400072245005300096856007600149490000700225520166000232       2025                            d  c03/20251 aErol Gelenbe1 aMohammed Nasereddin00aAdaptive attack mitigation for IoV Flood Attacks  u https://drive.google.com/file/d/1UPsgRyMDKUv4sT6oiOQQXxDgQ8bFTXQM/view0 v123 a<p>Gateway Servers for the Internet of Vehicles (IoV)&nbsp;must meet stringent Security and Quality of Service (QoS)&nbsp;requirements, including cyberattack protection, low delays and&nbsp;minimal packet loss, to offer secure real-time data exchange for&nbsp;human and vehicle safety and efficient road traffic management.&nbsp;Therefore, it is vital to protect these systems from cyberattacks&nbsp;with adequate Attack Detection (AD) and Mitigation mechanisms. Such attacks often include packet Floods that impair&nbsp;the QoS of the networks and Gateways and even impede the&nbsp;Gateways’ capability to carry out AD. Thus, this paper first&nbsp;evaluates these effects using system measurements during Flood&nbsp;attacks. It then demonstrates how a Smart Quasi-Deterministic&nbsp;Policy Forwarder (SQF) at the entrance of the Gateway can&nbsp;regulate the incoming traffic to ensure that the Gateway supports<br />
the AD to operate promptly during an attack. Since Flood attacks&nbsp;create substantial packet backlogs, we propose a novel Adaptive&nbsp;Attack Mitigation (AAM) system that is activated after an attack&nbsp;is detected to dynamically sample the incoming packet stream,&nbsp;determine whether the attack is continuing, and also drop batches&nbsp;of packets at the input to reduce the effects of the attack. The&nbsp;AAM is designed to minimize a cost function that includes the&nbsp;sampling overhead and the cost of lost benign packets. We show&nbsp;experimentally that the Optimum AAM approach is effective&nbsp;in mitigating attacks and present theoretical and experimental&nbsp;results that validate the proposed approach.</p>
01894nas a2200169   4500000000100000000000100001008004100002260001200043100001700055700001400072700001400086700001700100245007600117856007200193490000800265520145100273       2025                            d  c05/20251 aZhaozheng Li1 aJiahe Lan1 aZheng Yan1 aErol Gelenbe00aBackdoor attacks and defense mechanisms in federated learning: A survey  uhttps://www.sciencedirect.com/science/article/pii/S15662535250032150 v1233 a<p>Federated Learning (FL) is a distributed machine learning framework that enables the collaborative training of machine learning models by multiple entities. However, FL is vulnerable to various potential risks, especially backdoor attacks. A backdoor attack aims to implant hidden backdoors into a global model by compromising one or more clients and making them provide poisoned model updates. Consequently, the global model misclassifies inputs with triggers as adversary-desired classes/labels while performing well on benign inputs. Despite its severity, existing literature lacks a comprehensive review on backdoor attacks and their defense mechanisms of FL, especially for vertical FL. This paper comprehensively reviews and evaluates recent advances in backdoor attacks and defense mechanisms on FL. We first introduce foundational concepts about FL, backdoor attacks, and defense mechanisms, along with their respective security models. Then, we propose two sets of evaluation criteria that a sound backdoor attack and a defense mechanism should meet, respectively. After that, we provide taxonomies of existing backdoor attacks and defense mechanisms of FL and review them by employing the proposed criteria to evaluate their pros and cons. We also explore a positive application of backdoors in FL, i.e., backdoor-based watermarking. Finally, we discuss a number of open issues and suggest promising future research directions.</p>
01778nas a2200169   4500000000100000000000100001008004100002260001200043100001500055700001300070700001400083700001700097245009800114856005100212520133100263022001401594       2025                            d  c03/20251 aTingxi Luo1 aJie Wang1 aZheng Yan1 aErol Gelenbe00aGraph Neural Networks for Trust Evaluation: Criteria, State-of-the-Art, and Future Directions  uhttps://ieeexplore.ieee.org/document/10925363 3 a<p>The process of quantifying trust considers the factors that affect it, which can be applied to identify malicious behavior, reduce uncertainty, and facilitate decision-making. Traditional trust evaluation methods based on statistics and reasoning, rely heavily on domain knowledge, which limits their practical applications. Graph Neural Networks (GNNs) are a new Machine Learning (ML) paradigm that can revolutionize the evaluation of trust, by modeling relationships as graphs to simplify relevant data and automating end-to-end evaluation. Thus, a variety of GNN-based trust evaluation models have been developed for different applications. However, there is still a gap in the literature regarding a review on these advances with discussion about remaining challenges. To bridge this gap, we conduct the first review on GNN-based trust evaluation. We first propose a set of criteria in terms of trust-related attributes, correctness, functionality, and overhead. Then, we propose a taxonomy of existing GNN-based trust evaluation models, followed by a review using the proposed criteria to analyze their pros and cons. A quantitative analysis of the recent cutting-edge models is also provided. Based on the review and experimental results, we identify key challenges and suggest future research directions.&nbsp;</p>
  a0890-804401745nas a2200133   4500000000100000000000100001008004100002100001700043700001700060700001400077700001700091245009000108520141300198       2025                            d1 aZitong Zhang1 aHaoxiang Han1 aZheng Yan1 aErol Gelenbe00aA Survey on Cross-Shard Transaction Allocation Optimization and Processing Mechanisms3 a<p>Sharding is one of the most effective technologies for improving blockchain scalability by dividing a blockchain network into multiple shards that process transactions in parallel. However, many existing sharded blockchain systems can include a large number of cross-shard transactions, resulting in high communication overhead and processing latency. Two solutions have emerged to address these issues. One of them focuses on optimizing the allocation of transactions to reduce the cross-shard communication while ensuring workload balance. The other emphasizes the design of an efficient cross-shard transaction processing mechanism to reduce the latency caused by confirming cross-shard transactions. However, the literature still lacks a comprehensive review on the advances of Cross-shard Transaction Allocation Optimization and Processing (CTAOP) mechanisms, which would be a valuable addition to the literature. This paper is a comprehensive survey of existing CTAOP mechanisms, and first introduces cross-shard transactions and related concepts. Then, we propose two sets of criteria for evaluating the two types of CTAOP mechanisms, and provide a taxonomy of CTAOP mechanisms, followed by a thorough review based on our proposed criteria. A list of open issues is also highlighted, and corresponding future research directions are suggested to advance our understanding of CTAOP mechanisms.</p>
01666nas a2200133   4500000000100000000000100001008004100002260003900043100002400082700001700106245005900123856004100182520130900223                                       d  bIEEEaVilamoura, Algarve, Portugal1 aMohammed Nasereddin1 aErol Gelenbe00aParameter Sensitivity of AADRNN-Based Attack Detection  uhttps://ieee-iscc.computer.org/2026/3 a<p>The growing prevalence of cyberattacks against network-connected systems increases the need for accurate intrusion and attack detection (AD) systems that can react rapidly at the network access ports. Machine learning has often been used for AD because of the high accuracy it can offer, and its fast testing speed for real-time attack detection. On the other hand, the data sampling methods used to transform incoming traffic streams into data for real-time testing by AD systems can have an important effect on the detection accuracy, and are seldom discussed in the literature. Here, we address this often neglected aspect, with the Auto-Associative Dense Random Neural Network (AADRNN), a state-of-the-art AD system. After briefly reviewing the literature and comparing the AADRNN attack detector with other state-of-the-art approaches, this paper focuses on choosing the values of the data sampling parameters and the decision threshold to obtain the best accuracy from this AD method. Experiments regarding Botnet attack detection using the Mirai dataset, as well as synthetic flood attacks conducted on an experimental IoT test-bed are used to illustrate the results, and to guide the parameter choices that maximize the AADRNN’s True Positive Rate and minimize its False Positive Rate.</p>
01980nas a2200133   4500000000100000000000100001008004100002260002200043100001700065700001100082700001100093245010000104520164200204                                       d  c2025bIEEEaParis1 aErol Gelenbe1 aQ. Ren1 aZ. Yan00aA Probabilistic Dynamic Network Trust Model for IoT Systems with Lost Messages and Cyberattacks3 a<p>trust model, the Random Neural Network Trust Model (RNNTM),&nbsp;which incorporates the dynamics of trust formation in&nbsp;a network through a sequence of “votes” from each entity&nbsp;regarding all other entities. The model assures fairness among&nbsp;entities through a fixed replenishment rate of “voting rights”&nbsp;for each entity, whose voting rights are reduced each time the&nbsp;entity votes. A positive vote received by an entity increases<br />
its voting rights and its trustworthiness, while a negative vote&nbsp;reduces its voting rights and also its trustworthiness, and a&nbsp;non-negative integer represents each entity’s instantaneous “trust&nbsp;value”. An important property of the RNNTM is that an entity<br />
cannot express its trust or distrust of the other entities, and&nbsp;hence affect their trust values, when its trust level is down to&nbsp;zero, so that untrustworthy entities are not allowed to express&nbsp;trust or distrust. After developing the theoretical characteristics<br />
for the RNNTM model, this paper details its use to evaluate&nbsp;the trust value of multiple entities in a network of Internet of&nbsp;Things (IoT) devices and gateways, where&nbsp;cyberattacks against&nbsp;the gateways, and messages that should be received from IoT&nbsp;devices at regular intervals, modify the parameters that express&nbsp;the trust or distrust between entities. To illustrate its use for&nbsp;a network of interaction gateways, servers and user equipment,&nbsp;several detailed time-dependent simulations of the RNNTM are&nbsp;conducted in the presence of cyberattacks.&nbsp;</p>
01807nas a2200121   4500000000100000000000100001008004100002260002200043100001700065700002100082245004100103520154100144                                       d  c2025bIEEEaParis1 aErol Gelenbe1 aOmer Abdelrahman00aSignalling Storms and Taylor’s Law3 a<p>Signalling storms have occurred in several generations<br />
of mobile networks due to malevolent end users, as well as<br />
a consequence of service disruptions and system failures. Due to<br />
the massive increase in the volume of traffic and the diversity of<br />
end users and applications in 6G, including the massive access of<br />
the Internet of Things, signalling storms and other cyberattacks<br />
will require attention with research on adequate detection and<br />
mitigation schemes, as we move towards the next generation of<br />
mobile networks. Thus, in the present paper, we conduct discrete<br />
event simulations of a signalling storm, measure its effects in a<br />
simulated environment, and observe that it results in deviations<br />
from the well-known and naturally observed statistical property<br />
known as “Taylor’s Law”. We then discuss a mathematical model<br />
of storms, linking the fraction of malevolent or malfunctioning<br />
users to the rate of call establishment requests, and show that<br />
the mathematical model also exhibits a violation of Taylor’s<br />
Law. Since the detection of cyberattacks from network behaviour<br />
parameters is a challenging problem, we suggest that the violation<br />
of Taylor’s Law may be a useful tool for designing detectors for<br />
signalling attacks, or other network attacks or malfunctions.<br />
Index Terms—Attack Detection and Mitigation, 6G, Signalling<br />
Attacks, Taylor’s Law<br />
&nbsp;</p>
01461nas a2200109   4500000000100000000000100001008004100002100001700043700002400060245004700084520122000131       2025                            d1 aErol Gelenbe1 aMohammed Nasereddin00aData Driven Optimum Cyberattack Mitigation3 a<p>Gateways to the Internet of Things (IoT) are typically servers that communicate with IoT devices, providing them with low-latency services, and connecting them to the internet and other backbone networks. Since IoT devices are often simple and have limited storage and computational capabilities, gateways can be equipped with Attack Detection (AD) software to analyze incoming traffic, detect potential cyberattacks, and protect both the gateway and connected devices from threats that could overwhelm the system as a whole. This paper presents an enhanced gateway system that combines a traffic shaping technique with an attack detection module and an optimum attack mitigation scheme aimed at protecting the gateway and the overall system from cyberattacks. The optimum mitigation approach selects a sampling interval for the AD, that minimizes the total overhead of AD and mitigation. The proposed approach is implemented in a practical test-bed, so that the performance of the mitigation scheme may be evaluated in the presence of flood attacks. The experiments show its practical value and illustrate the agreement obtained between the analysis and the measurements obtained from several experiments.</p>
02133nas a2200157   4500000000100000000000100001008004100002260001200043100002000055700001700075700002400092700001600116245010500132490000800237520173000245       2025                            d  c11/20251 aJacob Bergquist1 aErol Gelenbe1 aMohammed Nasereddin1 aKarl Sigman00aMitigating massive access with Quasi-Deterministic Transmission: Experiments and stationary analysis0 v1703 a<p>The Massive Access Problem arises due to devices that forward packets simultaneously to servers in rapid succession, or by malevolent software in devices that flood network nodes with high-intensity traffic. To protect servers from such events, attack detection (AD) software is installed on servers, and the Quasi-Deterministic Transmission Policy (QDTP) has been proposed to “shape traffic” and protect servers, allowing attack detection to proceed in a timely fashion by delaying some of the incoming packets individually based on their arrival times. QDTP does not cause packet loss, and can be designed so that it does not increase end-to-end packet delay. Starting with measurements taken on an experimental test-bed where the QDPT algorithm is installed on a dedicated processor, which precedes the server itself, we show that QDPT protects the server from attacks by accumulating arriving packets at the input of the QDTP processor, then forwarding them at regular intervals to the server. We compare the behaviour of the server, with and without the use of QDTP, showing the improvement it achieves, provided that its “delay” parameter is correctly selected. We analyze the sample paths associated with QDTP and prove that when its delay parameter is chosen in a specific manner, the end-to-end delay of each packet remains unchanged as compared to an ordinary First-In-First-Out system. An approach based on stationary ergodic processes is developed for the stability conditions. Assuming mutually independent and identically distributed inter-arrival times, service times and QDTP delays, we exhibit the positive recurrent structure of a two-dimensional Markov process and its regeneration points.</p>
01576nas a2200121   4500000000100000000000100001008004100002260003400043100001500077700001700092245008700109520125800196       2024                            d  c12/2024bIEEEaKrakow, Poland1 aMert Nakip1 aErol Gelenbe00aAn Associated Random Neural Network Detects Intrusions and Estimates Attack Graphs3 a<p>Cyberattacks, especially Botnet Distributed Denial of Service (DDoS), increasingly target networked systems, compromise interconnected nodes by constantly spreading malware. In order to prevent these attacks in their early stages, which includes stopping the spread of malware, it is vital to identify compromised nodes and successfully predict potential attack paths. To this end, this paper proposes a novel system based on an Associated Random Neural Network (ARNN) that simultaneously detects intrusion at the network-level and estimates the network attack graph. In this system, ARNN is trained online to minimize problem-specific multi-task loss so that it identifies compromised network nodes, while the neural network connection weights also estimate the attack path. The performance of the method is calculated using the Kitsune attack dataset, showing that the method achieves a recall rate above 0.95 in estimating the network attack graph, and provides a near-perfect classification of compromised nodes. The ARNN-based system for dynamic and continuous estimation of compromised nodes and network attack graphs, can pave the way for enhancing security measures, and stopping Botnet DDoS attacks from spreading in networked systems.</p>
01691nas a2200121   4500000000100000008004100001260000900042100001700051700001500068700001500083245005300098520141800151       2026                            d  bIEEE1 aErol Gelenbe1 aBaran Gül1 aMert Nakip00aFederated Intrusion Detection for Smart Vehicles3 a<p>Cybersecurity threats endanger networked systems consisting of multiple nodes, such as the Internet of Things (IoT), Supply Chains and Smart Cars, due to the emergence of new types of unknown (zero-day) attacks against vulnerable IoT devices. While earlier research has shown that Machine Learning (ML)-based Intrusion Detection Systems (IDSs) enhance security, the efficacy and practicality of ML-based IDSs often require large training datasets based on private data. This challenge becomes more pronounced in the case of a distributed system, where each node must ensure the confidentiality of local data, yet faces attacks that are similar to those faced by other nodes in the system. Thus, this paper proposes a novel Decentralized Asynchronous IDS with Online Self-Supervised Federated Learning called the iDAF system, that improves overall security by providing collaborative confidentiality-preserving learning among all nodes. iDAF enables self-supervised learning without human intervention or large labelled data, and avoids time-consuming local training sessions. iDAF’s performance is evaluated for DoS and DDoS attacks in two different scenarios of Collaborating IoT Networks and Connected Smart Cars using three online datasets, and compared with five benchmark methods. The results show high intrusion detection accuracy with an acceptable computation time and low communication overhead.</p>01138nas a2200145   4500000000100000000000100001000000100002008004100003100001900044245009200063300001400155490000700169520080200176022001400978       2013                            d1 aPawel Foremski00aOn different ways to classify Internet traffic: a short review of selected publications  a119–1360 v253 aTraffic classification is an important tool for network management. It reveals the source of observed network traffic and has many potential applications in Quality of Service, network security, traffic visualization, and more. In the last decade, traffic classification evolved quickly due to the raise of peer-to-peer traffic. Nowadays, researchers still find new methods in order to withstand the rapid changes in the Internet. In this paper, we review 13 papers on traffic classification and related topics that were published during 2009-2012. We show diversity in recent algorithms and we highlight possible directions for the future research on traffic classification: relevance of multi-level classification, importance of experimental validation, and the need for common traffic datasets.  a1896-533400550nas a2200121   4500000000100000000000100001000000100002008004100003100001900044245006000063490000700123520029800130       2012                            d1 aPawel Foremski00aTracedump: A Novel Single Application IP Packet Sniffer0 v243 aThe article introduces a novel Internet diagnosis utility - an open source IP packet sniffer which captures TCP and UDP packets sent and received by a single Linux process only. Preliminary evaluation results are presented. The utility can be applied in the field of IP traffic classification.00841nas a2200109   4500000000100000000000100001008004100002260002600043100001900069245008200088520056100170       2011                            d  bPolitechnika Śląska1 aPawel Foremski00aStatistical, real-time classification of IP traffic in Linux operating system3 aThe thesis introduces a practical system for statistical classification of IP traffic. Two novel algorithms are applied and extended. They are based on feature vector classification using SVM. A software library written in C language is presented. Resultant system can monitor network interfaces in realtime and read off-line packet trace files. Simultaneous classification, system training, and performance evaluation is possible. The system yields very good results, in terms of quality and packet processing speed, achieving %TP>97 and %FP=0 on average.01166nas a2200157   4500000000100000000000100001008004100002260005000043100001900093700001700112700001400129700001400143245007300157300001100230520076700241       2015                            d  bSpringer International Publishing Switzerland1 aPawel Foremski1 aC. Callegari1 aM. Pagano1 aPiotr Gaj00aWaterfall traffic identification: optimizing classification cascades  a1–103 aThe Internet transports data generated by programs whch cause various phenomena in IP flows. By means of machine learning techniques, we can automatically discern between flows generated by different traffic sources and gain a more informed view of the Internet. In this paper, we optimize Waterfall, a promising architecture for cascade traffic classification. We present a new heuristic approach to optimal design of cascade classifiers. On the example of Waterfall, we show how to determine the order of modules in a cascade so that the classification speed in maximized, while keeping the number of errors and unlabeled flows at minimum. We validate our method experimentally on 4 real traffic datasets, showing significant improvements over random cascades.01223nas a2200145   4500000000100000000000100001008004100002100001900043700001700062700001400079245006200093856006200155520084600217022001401063       2014                            d1 aPawel Foremski1 aC. Callegari1 aM. Pagano00aDNS-Class: Immediate classification of IP flows using DNS  uhttps://www.iitis.pl/ pjf/pub/2014-dnsclass-preprint.pdf3 aNowadays we see a tremendous growth of the Internet, especially in terms of the amount of data being transmitted and new network protocols being introduced. This poses a challenge for network administrators, who need adequate tools for network management. Recent findings show that DNS can contribute valuable information on IP flows and improve traffic visibility in a computer network. In this paper, we apply these findings on DNS to propose a novel traffic classification algorithm with interesting features. We experimentally show that the information carried in domain names and port numbers is sufficient for immediate classification of a highly significant portion of the traffic. We present DNS-Class: an innovative, fast, and reliable flow-based traffic classification algorithm, which on average yields 99.8% of True Positives and  a1099-119001093nas a2200133   4500000000100000000000100001008004100002100001900043700001700062700001400079245007700093520077500170022001400945       2014                            d1 aPawel Foremski1 aC. Callegari1 aM. Pagano00aWaterfall: Rapid identification of IP flows using cascade classification3 aIn the last years network traffic classification has attracted much research effort, given that it represents the foundation of many Internet functionalities such as Quality of Service (QoS) enforcement, monitoring, and security. Nonetheless, the proposed works are not able to satisfactorily solve the problem, usually being suitable for only addressing a given portion of the whole network traffic and thus none of them can be considered an ultimate solution for network classification. In this paper, we address network traffic classification by proposing a new architecture-named Waterfall architecture-that, by combining several classification algorithms together according to a cascade principle, is able to correctly classify the whole mixture of network traffic.  a1865-092900637nas a2200121   4500000000100000000000100001008004100002100001900043700002100062700002200083245004800105520036200153       2014                            d1 aPawel Foremski1 aMichał Gorawski1 aKrzysztof Grochla00aSource Model of TCP Traffic in LTE Networks3 aWe propose a generator that represents traffic transmitted in wireless mobile networks. It is based on the measurements of traffic in large scale LTE networks. The proposed tool generates TCP flow sizes and duration. We verify the proposed generator by comparison with traces available in the literature and propose application in discrete event simulators.01743nas a2200181   4500000000100000000000100001000000100002008004100003260000600044100001900050700002100069700002200090700001800112245009100130490000700221520131900228022001401547       2015                            d  c91 aPawel Foremski1 aMichał Gorawski1 aKrzysztof Grochla1 aKonrad Połys00aEnergy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks0 v153 aThe paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown.  a1424-822001045nas a2200145   4500000000100000000000100001008004100002260005000043100001900093700002200112700001400134245007000148300001200218520066900230       2015                            d  aSpringer International Publishing Switzerland1 aPawel Foremski1 aKrzysztof Grochla1 aPiotr Gaj00aLTE or WiFi? Client-Side Internet link selection for smartphones.  a43–533 aCurrent mobile phones and tablets are equipped with two technologies for accessing the Internet: WiFi and Cellular. Deciding which of these two interfaces provides faster data transfer is often non-trivial, but most of the currently used devices use a simple priority scheme that prefers WiFi to Cellular. In this paper, we propose a novel system that automatically selects the best link available in the current user location. The system periodically probes the bandwidth available on both links and makes statistical predictions, while avoiding excessive data and battery usage. We experimentally validated our approach using a dedicated application for Android.00899nas a2200133   4500000000100000000000100001008004100002260000800043100002200051700001900073245007700092300001400169520058200183       2015                            d  bACM1 aKrzysztof Grochla1 aPawel Foremski00aDemo: Wireless Link Selection on Smartphone: Throughput vs Battery Drain  a190–1923 aThe method for automatic selection of wireless interface on smart phones is presented. We consider the selection between WiFi and LTE / 3G radios to maximize the throughput of the data transmission and minimize the amount of energy used to scan and measure the throughput of the network. The proposed selection algorithm defines when to measure and how to estimate the network throughput to balance between the accuracy of the selection and the battery drain caused by the measurements. The link speed estimation method optimized for LTE and WiFi networks is also demonstrated.00453nas a2200157   4500000000100000000000100001008004100002260001200043100001900055700002400074700001900098245008900117856006400206300001100270022001400281       2016                            d  c10/20161 aPawel Foremski1 aChristian Callegari1 aMichele Pagano00aWaterfall Traffic Classification: A Quick Approach to Optimizing Cascade Classifiers  uhttps://link.springer.com/article/10.1007/s11277-016-3751-5  a1–16  a0929-621200343nas a2200133   4500000000100000000000100001008004100002260002600043100001900069700001700088700001800105245005500123856003100178       2016                            d  c11/2016aSanta Monica1 aPawel Foremski1 aDavid Plonka1 aArthur Berger00aEntropy/IP: Uncovering Structure in IPv6 Addresses  uhttp://www.entropy-ip.com/01572nas a2200205   4500000000100000000000100001008004100002260002500043100001800068700002000086700001900106700001500125700002200140700002000162700001800182700001600200245007100216856003500287520104400322       2018                            d  bACMaBoston, MA, USA1 aOliver Gasser1 aQuirin Scheitle1 aPawel Foremski1 aQasim Lone1 aMaciej Korczynski1 aStephen Strowes1 aLuuk Hendriks1 aGeorg Carle00aClusters in the Expanse: Understanding and Unbiasing IPv6 Hitlists  uhttps://ipv6hitlist.github.io/3 a<p>Network measurements are an important tool in understanding the Internet. Due to the expanse of the IPv6 address space, exhaustive scans as in IPv4 are not possible for IPv6. In recent years, several studies proposed to use target lists of IPv6 addresses, called hitlists. In this paper, we show that addresses in IPv6 hitlists are heavily clustered. We present novel techniques that allow to push IPv6 hitlists from quantity to quality. We perform a longitudinal active measurement study over 6 months, targeting more than 50 M addresses. We develop a rigorous method to detect aliased prefixes, which identifies 1.5% of our prefixes as aliased, pertaining to about half of our target addresses. Using entropy clustering, we group the entire hitlist into just 6 distinct addressing schemes. Furthermore, we perform client measurements by leveraging crowdsourcing. To encourage reproducibility in network measurement research and serve as a starting point for future IPv6 studies, we publish source code, analysis tools, and data.</p>
01816nas a2200133   4500000000100000000000100001008004100002260002800043100001900071700001800090700001800108245004800126520150800174       2019                            d  c10/2019bACMaAmsterdam1 aPawel Foremski1 aOliver Gasser1 aGiovane Moura00aDNS Observatory: The Big Picture of the DNS3 a<p>The Domain Name System (DNS) is thought of as having the simple-sounding task of resolving domains into IP addresses. With its stub resolvers, different layers of recursive resolvers, authoritative nameservers, a multitude of query types, and DNSSEC, the DNS ecosystem is actually quite complex.</p>

<p>DNS Observatory provides a bird's-eye view and allows to analyze the big picture of DNS. As data source DNS Observatory leverages globally distributed DNS probes acquiring a peak of 200K DNS queries per second between recursive resolvers and authoritative nameservers. For each observed query we extract traffic features, aggregate them, and track the top k DNS objects.</p>

<p>This allows us to characterize DNS deployments and evaluate the median response delays of DNS queries, where we find that the top 10% nameservers (which handle about half the traffic) have indeed a shorter response time than less popular nameservers. We also leverage DNS Observatory to show correlations between decreasing TTLs and increasing DNS traffic. Furthermore, the TTL data allows us to anticipate upcoming changes in the DNS infrastructure. Another aspect that we analyze in depth is the effect of the Happy Eyeballs algorithm in combination with low negative caching TTLs, which results in a share of up to 90% empty responses for some domains. Finally, we propose actionable measures to improve uncovered DNS issues and shortcomings and we offer interested researchers access to DNS Observatory.</p>
01839nas a2200133   4500000000100000000000100001008004100002260004100043100001900084700001800103700001800121245004800139520151800187       2019                            d  c10/2019bACMaAmsterdam, Netherlands1 aPawel Foremski1 aOliver Gasser1 aGiovane Moura00aDNS Observatory: The Big Picture of the DNS3 a<p>The Domain Name System (DNS) is thought of as having the simple-sounding task of resolving domains into IP addresses. With its stub resolvers, different layers of recursive resolvers, authoritative nameservers, a multitude of query types, and DNSSEC, the DNS ecosystem is actually quite complex.<br />
<br />
DNS Observatory provides a bird's-eye view and allows to analyze the big picture of DNS. As data source DNS Observatory leverages globally distributed DNS probes acquiring a peak of 200K DNS queries per second between recursive resolvers and authoritative nameservers. For each observed query we extract traffic features, aggregate them, and track the top k DNS objects.<br />
<br />
This allows us to characterize DNS deployments and evaluate the median response delays of DNS queries, where we find that the top 10% nameservers (which handle about half the traffic) have indeed a shorter response time than less popular nameservers. We also leverage DNS Observatory to show correlations between decreasing TTLs and increasing DNS traffic. Furthermore, the TTL data allows us to anticipate upcoming changes in the DNS infrastructure. Another aspect that we analyze in depth is the effect of the Happy Eyeballs algorithm in combination with low negative caching TTLs, which results in a share of up to 90% empty responses for some domains. Finally, we propose actionable measures to improve uncovered DNS issues and shortcomings and we offer interested researchers access to DNS Observatory.</p>
01867nas a2200205   4500000000100000000000100001008004100002260002800043100001900071700002100090700001900111700002000130700002700150700001800177700002900195700001800224700002100242245008900263520130900352       2022                            d  c08/2022bUSENIXaBoston1 aXander Bouwman1 aVictor Le Pochat1 aPawel Foremski1 aTom Van Goethem1 aCarlos Hernandez-Ganan1 aGiovane Moura1 aSamaneh Tajalizadehkhoob1 aWouter Joosen1 aMichel van Eeten00aHelping hands: Measuring the impact of a large threat intelligence sharing community3 aWe tracked the largest volunteer security information sharing community known to date: the COVID-19 Cyber Threat Coalition, with over 4,000 members. This enabled us to address long-standing questions on threat information sharing. First, does collaboration at scale lead to better coverage? And second, does making threat data freely available improve the ability of defenders to act? We found that the CTC mostly aggregated existing industry sources of threat information. User-submitted domains often did not make it to the CTC’s blocklist as a result of the high threshold posed by its automated quality assurance using VirusTotal. Although this ensured a low false positive rate, it also caused the focus of the blocklist to drift away from domains related to COVID-19 (1.4%-3.6%) to more generic abuse, such as phishing, for which established mitigation mechanisms already exist. However, in the slice of data that was related to COVID-19, we found promising evidence of the added value of a community like the CTC: just 25.1% of these domains were known to existing abuse detection infrastructures at time of listing, as compared to 58.4% of domains on the overall blocklist. From the unique experiment that the CTC represented, we draw three lessons for future threat data sharing initiatives.
01398nas a2200145   4500000000100000000000100001000000100002008004100003260004400044100001900088700001800107700001700125245006600142520104400208       2021                            d  c03/2021bAkamai Technologies, Inc.aUSA1 aPawel Foremski1 aArthur Berger1 aDavid Plonka00aInternet address structure analysis, and applications thereof3 aAn analysis system automates IP address structure discovery by deep analysis of sample IPv6 addresses using a set of computational methods, namely, information-theoretic analysis, machine learning, and statistical modeling. The system receives a sample set of IP addresses, computes entropies, discovers and mines address segments, builds a network model of address segment inter-dependencies, and provides a graphical display with various plots and tools to enable a network analyst to navigate and explore the exposed IPv6 address structure. The structural information is then applied as input to applications that include: (a) identifying homogeneous groups of client addresses, e.g., to assist in mapping clients to content in a CDN; (b) supporting network situational awareness efforts, e.g., in cyber defense; (c) selecting candidate targets for active measurements, e.g., traceroutes campaigns, vulnerability assessments, or reachability surveys; and (d) remotely assessing a network's addressing plan and address assignment policy.01677nas a2200157   4500000000100000000000100001008004100002260002800043100001500071700001900086700001800105245006700123856003600190520127500226020001801501       2024                            d  c07/2024bACMaSingapore1 aLars Prehn1 aPawel Foremski1 aOliver Gasser00aKirin: Hitting the Internet with Distributed BGP Announcements  uhttps://kirin-attack.github.io/3 a<p>The Internet is a critical resource in the daily life of billions of users. To support the growing number of users and their increasing demands, operators continuously scale their network footprint–-e.g., by joining Internet Exchange Points (IXPs)–-and adopt relevant technologies–-such as IPv6–-which provides a vastly larger address space than its predecessor.In this paper, we revisit prefix de-aggregation attacks in the light of these two changes and introduce Kirin–-an advanced BGP prefix de-aggregation attack that announces millions of IPv6 routes via thousands of IXP connections to overflow the memory of routers within remote ASes. Kirin's highly distributed nature allows it to bypass traditional route-flooding defense mechanisms, such as per-session prefix limits or route flap damping.We analyze Kirin's theoretical feasibility by formulating it as a mathematical optimization problem, test for practical hurdles by deploying enough infrastructure to perform a micro-scale Kirin attack using 4 IXPs, and validate our assumptions via BGP data analysis, real-world measurements, and router testbed experiments. Despite its low deployment cost, we find that Kirin may inject lethal amounts of routes into the routers of thousands of ASes.</p>
  a979840070482600386nas a2200145   4500000000100000000000100001008004100002260003000043100001900073700001700092700002000109700002200129245006700151020002200218       2019                            d  cJune 2019bSpringer, Cham1 aGodlove Kuaban1 aErnest Molua1 aPiotr Czekalski1 aKrzysztof Grochla00aAn Architectural Framework Proposal for IoT Driven Agriculture  a978-3-030-21951-200435nas a2200145   4500000000100000000000100001008004100002260003000043100001900073700002000092700001700112700002000129245011800149020002200267       2020                            d  cJune 2020bSpringer, Cham1 aGodlove Kuaban1 aBhavneet Soodan1 aRakesh Kumar1 aPiotr Czekalski00aPerformance Evaluations of a Cloud Computing Physical Machine with Task Reneging and Task Resubmission (Feedback)  a978-3-030-50719-000404nas a2200145   4500000000100000000000100001008004100002100001900043700001700062700002000079700002000099245011300119300002000232490000600252       2020                            d1 aGodlove Kuaban1 aRakesh Kumar1 aBhavneet Soodan1 aPiotr Czekalski00aA Multi-Server Queuing Model With Balking and Correlated Reneging With Application in Health Care Management  a169623–1696390 v801803nas a2200217   4500000000100000000000100001008004100002260002700043100002100070700001700091700001900108700002100127700002400148700002300172700002100195700001900216700001500235700001900250245007400269520124200343       2020                            d  bIEEEaUppsala, Sweeden1 aKrzysztof Tokarz1 aP. Czekalski1 aGabriel Drabik1 aJarosław Paduch1 aSalvatore Distefano1 aRiccardo Di Pietro1 aGiovanni Merlino1 aCarlo Scaffidi1 aRaivo Sell1 aGodlove Kuaban00aInternet of Things Network Infrastructure for The Educational Purpose3 a<p><span style="background-color:rgb(255, 255, 255); color:rgb(51, 51, 51); font-family:sans-serif; font-size:15px">In this innovative practice full paper we present the implementation of the distant laboratory for the Internet of Things teaching and training. The recent outbreak of the SARS-COV-2 virus and related COVID-19 pandemic throughout the world has caused governments across the world to shut down schools and universities, to slow down the spread of the coronavirus that is causing the disease. As a result, some universities and schools have switched from physical classrooms to virtual or online classrooms. This approach is working well for theoretical subjects and courses, but it is not straight forward in the case of laboratory subjects and courses that require access to hardware resources. The IOT-OPEN.EU remote laboratory infrastructure presented in this paper is a timely solution. In this paper, we present current advances in distant learning, distant laboratory models, and the IOT-OPEN.EU remote laboratory implemented as part of the IOT-OPEN.EU ERASMUS+ project, along with short analysis on current advances in distant learning, where students are interacting with physical hardware remote way.</span></p>
01634nas a2200145   4500000000100000000000100001008004100002260003300043100001900076700002000095700001700115700002000132245012100152520121500273       2021                            d  bIEEEaCape Town-South Africa1 aGodlove Kuaban1 aBhavneet Soodan1 aRakesh Kumar1 aPiotr Czekalski00aAnalysis of the Performance of a Cloud Computing Processing Queue with Correlated Reneging of Tasks and Resubmission3 a<p>The Quality of Service (QoS) experienced by the packets that constitute a cloud computing task does not only depend on the performance of the cloud computing network infrastructure at the data centre but also on the performance of the access networks and the internet core networks traversed by the packets before the task is fully submitted to the cloud computing or processing servers for execution or processing. Sometimes, some of the tasks that are submitted and are waiting in queues to be processed could be removed from the queues without being processed (task reneging or dropping). Removing tasks from the queue could result from the user's impatience, missing execution deadline, security reasons, or an active queue management strategy. The reneged or dropped tasks could be resubmitted depending on the reason for which it was dropped. The resubmission of tasks decreases QoS and increases the energy consumption overhead as more energy is required to transport the resubmitted tasks through the access, core, and data centre networks. In this paper, we propose a multiserver queueing model with correlated reneging and resubmission to analyse the performance of a cloud computing server.</p>
02123nas a2200157   4500000000100000000000100001008004100002100001700043700002000060700001900080700002000099700001800119245010300137490001000240520171500250       2021                            d1 aRakesh Kumar1 aBhavneet Soodan1 aGodlove Kuaban1 aPiotr Czekalski1 aSapana Sharma00aPerformance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging0 v2091 3 a<p>Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like a vera ge queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging</p>
01625nas a2200157   4500000000100000000000100001008004100002260001900043100001900062700002000081700001700101700002000118245013100138520117600269020002201445       2022                            d  bIEEEaMaldives1 aGodlove Kuaban1 aBhavneet Soodan1 aRakesh Kumar1 aPiotr Czekalski00aA Queueing-theoretic Analysis of the Performance of a Cloud Computing Infrastructure: Accounting for Task Reneging or Dropping3 a<p>Cloud computing has revolutionized the information technology era. It offers high-speed computing, storage, and ICT resources on demand. One of the significant challenges in cloud computing is the impact of impatient users or request (task) reneging. When a request has been compromised, missed its execution deadline, or depends on other rejected ones, it must be removed from the queue without being processed. The reneging or removal of tasks from queues may trigger the reneging or removal of other tasks that depend of them. We refer to this reneging of requests from the load balancing or computing queues as correlated reneging. We presented the performance analysis of a network of queues that constitute a queueing model of a simplified cloud computing infrastructure. We show the relationship between the load, delay, and probability of buffer saturation (blocking probability). It is seen that at about 80% utilization, a small increase in utilization (due to a small increase in the arrival rate Δλ for a fixed service rate μ) results in a sharp increase in the delay experienced by the tasks, and on the probability of task rejection or blocking.</p>
  a978-1-6654-7095-702195nas a2200217   4500000000100000000000100001008004100002260003300043100001900076700001800095700002000113700002100133700001800154700002300172700002000195700002000215700001500235245011200250520159300362020002201955       2022                            d  bIEEEaPrague, Czech Republic1 aGodlove Kuaban1 aMateusz Nowak1 aPiotr Czekalski1 aKrzysztof Tokarz1 aJulius Tangka1 aKjartan Siggursson1 aAgris Nikitenko1 aKarlis Berkolds1 aRaivo Sell00aAn IoT Course Program to Foster the Adoption of IoT Driven Food and Agriculture in Sub-Saharan Africa (SSA)3 a<p>The Internet of Things (IoT) technologies, together with other technological advances such as big data analytics, Artificial Intelligence (AI), automation, and unmanned electrical vehicles (e.g., self-driving vehicles and drones), are currently being used to transform the food and agriculture industry to increase yields significantly with minimal damage to the environment. These recent advances in Information and Communication Technologies and advances in biotechnology are driving the transition into the fourth agricultural revolution or agriculture 4.0. Some research studies on the adoption of IoT in the agriculture sector in Sub-Saharan Africa concluded that some of the reasons the rate of adoption of IoT in Africa is still very low are gaps in knowledge, skills, finance, and infrastructure. The knowledge and skills gaps are partly a result of an educational curriculum that is not up to date with current advances in the food and agricultural industry. To foster IoT adoption in the food and agriculture sectors and other sectors or industries in Sub-saharan Africa, we propose an IoT course program consisting of lectures and laboratory modules. The course is accompanied by textbooks, video lectures that are freely available on Edx, and remote online laboratories with the hardware resources hosted in some European countries but accessible through the Internet. These courses can also be adapted by vocational training schools, Higher Institutes, and universities in Sub-Saharan Africa or could be used by professionals and enthusiasts to improve their skills.</p>
  a978-1-6654-7087-201451nas a2200169   4500000000100000000000100001008004100002260001500043100001800058700001700076700001900093700002000112700001800132245014000150490000700290520098400297       2024                            d  c01/05/20241 aSapana Sharma1 aRakesh Kumar1 aGodlove Kuaban1 aBhavneet Soodan1 aPradeep Singh00aPerformance and Cost Evaluation of an Adaptive Queuing System with Customer Reneging and Retention: Steady-state and Transient Analysis0 v153 a<pre>
Customers are often required to wait when they arrive at service facilities and see the servers are busy, or when they find other customers who arrived earlier waiting. The longer customers wait, the more dissatisfied they are likely to be and may leave the queue without receiving service (reneging). The objective of the service provider is to improve the quality of service in order to minimise the possibility of customer reneging since it increases cost and reduces revenue. Therefore, a trade-off between performance and cost should be considered when designing, planning and reducing the queues at service facilities. In this paper, we propose an adaptive queuing model with the retention of reneging customers. We derive the steady-state and transient-state performance parameters, and also discuss performance and cost evaluation. We demonstrate the utility of the model in the evaluation of waiting lines in the service industry using numerical examples.</pre>
01628nas a2200181   4500000000100000000000100001008004100002260000900043100001900052700002000071700001900091700001500110700002000125700002000145700001700165245012300182520114100305       2023                            d  bIEEE1 aGodlove Kuaban1 aPiotr Czekalski1 aOnyeka Nwobodo1 aRaivo Sell1 aAgris Nikitenko1 aKarlis Berkolds1 aKenedy Tanko00aSmart Sustainable Agrivoltaics Systems: The Future of Sustainable Agricultural Technology (Agri-tech) and Green Energy3 a<p>Agricultural productivity depends primarily on energy, water, and land resources, which are increasingly becoming more scarce and expensive. Electricity generation with photovoltaic (PV) solar energy technology requires significant amounts of space, especially in densely populated countries, generating a societal debate about allocating land (that may have alternative uses) for deploying PV systems. Rather than dedicating vast amounts of agricultural land to be used as solar farms, PV systems are deployed in agricultural lands so that a given piece of land can be used for agriculture and energy generation (the so-called agrivoltaics). A framework based on systems thinking is essential for the design and operation of smart, sustainable agrivoltaics systems to meet the design goals or to satisfy the expectation of all stakeholders (farmers, energy developers, policymakers, and local community members that may be impacted). In this paper, we propose a system-based conceptual and design framework for smart, sustainable agrivoltaics. We also discuss the benefits of agrivoltaics and the challenges to their adoption.</p>
01871nas a2200157   4500000000100000000000100001008004100002100001900043700001900062700002000081700001000101700001900111700002000130245010700150520145600257       2024                            d1 aOnyeka Nwobodo1 aGodlove Kuaban1 aTomasz Kukuczka1 aKamil1 aWereszczyński1 aKrzysztof Cyran00aAnalysis of Marker and SLAM-based Tracking for Advanced Augmented Reality (AR)-based Flight Simulation3 a<p>Augmented reality (AR)-based flight simulation reshapes how pilots are trained, offering an immersive environment where commercial<br />
and fighter pilots can be trained at low cost with minimal use of fuel and safety concerns. This study conducts a pioneering comparative analysis of marker-based tracking and SLAM technologies within the Microsoft HoloLens 2 platform, mainly focusing on their efficacy in landing manoeuvre simulations. Our investigation incorporates an experimental setup where marker-based tracking overlays interactive video tutorials onto a simulated cockpit, enhancing the realism and effectiveness of landing procedures. The experiment demonstrates that marker-based systems ensure high precision within 5 cm and 15 cm from the HoloLens 2 camera, proving indispensable for procedural training that requires exact overlay precision. Conversely, the native SLAM algorithm, while lacking the same level of precision, offers flexibility and adaptability by accurately mapping the cockpit and superimposing virtual information in dynamic, markerless conditions. The study juxtaposes these technologies, revealing a trade-off between precision and adaptability, and suggests an integrative approach to leverage their respective strengths. Our findings provide pivotal insights for developers and training institutions to optimize AR flight simulation training, contributing to advanced, immersive pilot training programs.</p>
01942nas a2200169   4500000000100000000000100001008004100002260004100043100001900084700001900103700002000122700002500142700002000167245010700187520145600294020002201750       2024                            d  c29/06/2024bSpringeraMálaga, Spain1 aOnyeka Nwobodo1 aGodlove Kuaban1 aTomasz Kukuczka1 aKamil Wereszczyński1 aKrzysztof Cyran00aAnalysis of Marker and SLAM-based Tracking for Advanced Augmented Reality (AR)-based Flight Simulation3 a<p>Augmented reality (AR)-based flight simulation reshapes how pilots are trained, offering an immersive environment where commercial<br />
and fighter pilots can be trained at low cost with minimal use of fuel and safety concerns. This study conducts a pioneering comparative analysis of marker-based tracking and SLAM technologies within the Microsoft HoloLens 2 platform, mainly focusing on their efficacy in landing manoeuvre simulations. Our investigation incorporates an experimental setup where marker-based tracking overlays interactive video tutorials onto a simulated cockpit, enhancing the realism and effectiveness of landing procedures. The experiment demonstrates that marker-based systems ensure high precision within 5 cm and 15 cm from the HoloLens 2 camera, proving indispensable for procedural training that requires exact overlay precision. Conversely, the native SLAM algorithm, while lacking the same level of precision, offers flexibility and adaptability by accurately mapping the cockpit and superimposing virtual information in dynamic, markerless conditions. The study juxtaposes these technologies, revealing a trade-off between precision and adaptability, and suggests an integrative approach to leverage their respective strengths. Our findings provide pivotal insights for developers and training institutions to optimize AR flight simulation training, contributing to advanced, immersive pilot training programs.</p>
  a978-3-031-63783-401952nas a2200169   4500000000100000000000100001008004100002260001500043100001900058700001900077700001900096700002000115700002200135245012100157490000700278520149700285       2024                            d  c16/10/20241 aGodlove Kuaban1 aValery Nkemeni1 aOnyeka Nwobodo1 aPiotr Czekalski1 aFabien Mieyeville00aInternet of Things Adoption in Technology Ecosystems within the Central African Region: The Case of Silicon Mountain0 v163 a<p>The Internet of Things (IoT) has emerged as a transformative technology with the potential to revolutionize various sectors and industries worldwide. Despite its global significance, the adoption and implementation of IoT technologies in emerging technology ecosystems within the Central African region still need to be studied and explored. This paper presents a case study of the Silicon Mountain technology ecosystem, located in the Fako division of the southwest region of Cameroon, focusing on the barriers and challenges to adopting and integrating IoT technologies within this emerging tech ecosystem. Through a survey research method, we investigate the factors influencing IoT adoption in the Silicon Mountain tech ecosystem, including technological, economic, social, and regulatory factors. Our study reveals key insights into the current state of IoT adoption, opportunities for growth and innovation, and IoT adoption challenges. Key among the challenges identified for impeding IoT uptake were issues related to standardization and financial resources, labor shortage in the industry, educational and knowledge gaps, market challenges, government policies, security and data privacy concerns, and inadequate power supply. Based on our findings, we provide recommendations for policymakers, industry stakeholders, and academic institutions to promote and facilitate the widespread adoption of IoT technologies in Silicon Mountain and the Central African region at large.</p>
01577nas a2200193   4500000000100000000000100001008004100002260001300043100001600056700001900072700001900091700001400110700001900124700001800143700001900161700002200180245010000202520108100302                                       d  bSpringer1 aMangeh Jaja1 aValery Nkemeni1 aGodlove Kuaban1 aAkum Acha1 aOnyeka Nwobodo1 aEric Djomadji1 aPierre Tsafack1 aPierre Brosselard00aA Comprehensive Crop Recommendation System Lever- aging Internet of Things and Machine Learning3 a<p>In the pursuit of sustainable agriculture and food security, the efficient use of soil nutrients is paramount. This paper presents the development of an Internet of Things (IoT) and Machine Learning (ML)-based system for crop recommendation. The system features a sensor that leverages visible infrared spectroscopy to accurately determine the nitrogen-phosphorus-potassium (NPK) concentration in soil. Integrated into a sensor node comprising of the ESP32 system-on-chip&nbsp;(SoC), this device collects real-time soil data and transmits it to the cloud. In the cloud, a web application employs a machine learning model developed from the random forest ML algorithm to analyze the NPK data and recommend the most suitable crops for cultivation on the given land. The developed model achieved a 91.1% overall accuracy. This approach not only optimizes crop selection but also promotes sustainable agricultural practices by ensuring that crops are matched to the soil's nutrient profile, consequently leading to improved crop yield while reducing environmental impact.</p>
01547nas a2200229   4500000000100000000000100001008004100002260001500043100001900058700002200077700001900099700002000118700002100138700002000159700001800179700001800197700001900215700002700234245009800261490000700359520095100366       2024                            d  c31/10/20241 aValery Nkemeni1 aFabien Mieyeville1 aGodlove Kuaban1 aPiotr Czekalski1 aKrzysztof Tokarz1 aWirnkar Nsanyuy1 aEric Djomadji1 aMusong Katche1 aPierre Tsafack1 aBartłomiej Zieliński00aEvaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks0 v243 a<p>Battery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network’s lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN’s lifetime. By categorizing existing strategies into node energy reduction, net-<br />
work energy balancing, and energy replenishment, this study assesses their effectiveness when im- plemented in LWSN applications, providing valuable insights to assist engineers during the design of green and energy-efficient LWSN monitoring systems.</p>
01867nas a2200181   4500000000100000000000100001008004100002260001500043100001900058700002500077700001900102700002600121700002000147245011500167490000700282520138200289022001401671       2024                            d  c14/11/20241 aOnyeka Nwobodo1 aKamil Wereszczyński1 aGodlove Kuaban1 aPRZEMYSłAW SKUROWSKI1 aKRZYSZTOF CYRAN00aAn Adaptation of Fitts’ Law for Performance Evaluation and Optimization of Augmented Reality (AR) Interfaces0 v123 a<p>There is growing widespread adoption of augmented reality (AR) in tech-driven industries and sectors of society, such as medicine, gaming, flight simulation, education, interior design and modelling, entertainment, construction, tourism, repair and maintenance, public safety, agriculture, and quantum computing. However, ensuring smooth and intuitive interactions with augmented objects is challenging, requiring practical performance evaluation and optimization models to assess and improve users’ experiences with AR-enhanced systems. In this paper, we apply Fitts’ Law to model and predict interaction task difficulty with objects distributed across four spatial quadrants. We use genetic optimization algorithms to fine-tune Fitts’s Law parameters, achieving a model that significantly enhances predictive accuracy. Our optimized model demonstrates an approximately 40% reduction in interaction task difficulty across all quadrants, leading to a more ergonomic and intuitive user interface. This study contributes to the Human-Computer Interaction (HCI) field by offering a refined metric for evaluating and optimizing AR interfaces and addressing the unique challenges of three-dimensional interaction environments. Therefore, we propose a practical framework for the performance evaluations and optimization of augmented reality and other user interfaces.</p>
  a2169-353600376nas a2200133   4500000000100000000000100001008004100002100001900043700001900062700001900081700002500100700002000125245009700145       2025                            d1 aOnyeka Nwobodo1 aGodlove Kuaban1 aValery Nkemeni1 aKamil Wereszczyński1 aKRZYSZTOF CYRAN00aA Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators01796nas a2200109   4500000000100000000000100001008004100002260003700043100001900080245008200099520150500181       2025                            d  c02/07/2025bIEEEaAbu Dhabi, UAE1 aGodlove Kuaban00aAn Analytical Design Framework for Dimensioning Solar-powered Green IoT Nodes3 a<p>The rapid adoption of the Internet of Things (IoT) across various industries has significantly enhanced productivity and efficiency. However, the widespread deployment of battery-powered IoT devices raises concerns about energy sustainability, battery longevity, and environmental impact. Frequent battery replacements pose logistical challenges, particularly in large-scale networks and remote locations. To address these issues, the Green IoT (G-IoT) design framework has been introduced, emphasizing energy-efficient strategies such as duty cycling, transceiver optimization, energy-aware routing, and the integration of renewable energy sources like photovoltaic (PV) energy harvesting. Despite advancements in G-IoT, a major challenge remains: the dynamic and unpredictable nature of both IoT energy consumption and harvested energy. In this paper, we propose a design framework for Green IoT that models the interplay between energy harvesting, storage, and consumption processes. By incorporating time-varying energy dynamics, the proposed framework enables efficient dimensioning of energy harvesters and storage systems, ensuring optimal energy management in IoT networks. This approach allows for the evaluation of various energy-saving strategies and enhances the long-term sustainability of IoT deployments. Numerical examples and simulation results demonstrate the effectiveness of the framework in maintaining energy balance and extending the operational lifetime of IoT nodes.</p>
02128nas a2200157   4500000000100000000000100001008004100002260003900043100001900082700001900101700002500120700002000145245012700165520165600292020002201948       2025                            d  c07/2025bSpringer, ChamaSingapore1 aOnyeka Nwobodo1 aGodlove Kuaban1 aKamil Wereszczyński1 aKRZYSZTOF CYRAN00aEnhancing Learning in Augmented Reality (AR): A Deep Learning Framework for Predicting Memory Retention in AR Environments3 a<p>The integration of Artificial Intelligence (AI) with Augmented Reality (AR) has transformed human-computer interaction, offering new opportunities for immersive learning and cognitive assessment. However, the relationship between user engagement in AR environments and memory retention remains underexplored. This study proposes an AI-driven framework for predicting memory retention using behavioural interaction data captured through Microsoft HoloLens 2 sensors. The model estimates the likelihood of object recall in AR-based learning environments by analyzing key interaction metrics such as gaze duration, interaction frequency, revisit counts, and head movement stability.<br />
To validate the AI predictions, we compared model-generated retention scores with user-reported recall, demonstrating a strong alignment between predicted and actual memory performance. Our findings align with established cognitive theories, indicating that increased interaction and attentional engagement enhance memory retention. Furthermore, comparisons with prior research on perceptual judgments and spatial memory reinforce the model’s effectiveness in capturing real-world cognitive processes. This study introduces a scalable, non-invasive approach to cognitive modeling, bridging AI-driven analytics with AR-based learning. The results have broad implications for education, medical training, AR-based flight simulation training, and workforce development, where optimizing learning efficiency is crucial. By leveraging AI for real-time memory prediction, this research paves the way for more adaptive and personalized AR learning experiences.</p>
  a978-3-031-97573-801362nas a2200205   4500000000100000000000100001008004100002260001500043100001500058700002000073700002100093700001900114700002000133700002000153700001700173245007000190490000700260520087500267022001401142       2025                            d  c07/05/20251 aRaivo Sell1 aPiotr Czekalski1 aKrzysztof Tokarz1 aGodlove Kuaban1 aAgris Nikitenko1 aKarlis Berkolds1 aLukasz Lipka00aAn international hands-on IoT education platform towards Industry0 v743 a<p>This paper presents VREL NextGen, an international Internet of Things (IoT) education platform developed through European university collaboration. The platform addresses hands-on IoT education needs in the Industry 5.0 context through three distinct implementations: a microcontroller laboratory at the Silesian University of Technology, a building management sensor network at Riga Technical University, and a human-robot collaboration platform at Tallinn University of Technology. The system enables remote access to physical hardware while promoting resource sharing and standardized educational approaches. Implementation results demonstrate effective integration of theoretical knowledge with practical skills, supporting both fundamental engineering concepts and advanced IoT applications while emphasizing human-centric design principles of Industry 5.0</p>
  a1736-753001966nas a2200157   4500000000100000000000100001008004100002260001500043100001900058700001900077700002000096245012100116856004600237490000700283520151800290       2025                            d  c15/05/20251 aGodlove Kuaban1 aValery Nkemeni1 aPiotr Czekalski00aAn Analytical Framework for Optimizing the Renewable Energy Dimensioning of Green IoT Systems in Pipeline Monitoring  uhttps://www.mdpi.com/1424-8220/25/10/31370 v253 aThe increasing demand for sustainable and autonomous monitoring solutions in critical infrastructure has driven interest in Green Internet of Things (G-IoT) systems. This paper presents an analytical and experimental framework for designing energy-efficient, self-sustaining pipeline monitoring systems that leverage renewable energy harvesting and low-power operation techniques. We propose a hybrid approach combining solar energy harvesting with energy-saving strategies such as adaptive sensing, duty cycling, and distributed computing to extend the lifetime of IoT nodes without human intervention. Using real-world irradiance data and energy profiling from a prototype testbed, we analyze the impact of solar panel sizing, energy storage capacity, energy-saving strategies, and energy leakage on the energy balance of IoT nodes. The simulation results show that, with optimal dimensioning, harvested solar energy can sustain pipeline monitoring operations over multi-year periods, even under variable environmental conditions. We investigated the influence of design parameters such as duty cycling, solar panel area, the capacity of the energy storage system, and the energy leakage coefficient on energy performance metrics such as the autonomy or lifetime of the node (time required to drain all the stored energy), which is an important design object. This framework provides practical design insights for the scalable deployment of G-IoT systems in energy-constrained outdoor environments.</span></p>
02899nas a2200181   4500000000100000000000100001008004100002260002700043100002000070700002100090700001900111700001500130700002000145700002000165700001800185245008700203520242700290       2025                            d  bElsevieraOsaka, Japan1 aPiotr Czekalski1 aKrzysztof Tokarz1 aGodlove Kuaban1 aRaivo Sell1 aAgris Nikitenko1 aKarlis Berkolds1 aŁukasz Lipka00aDesign and Implementation of a Next-Generation Remote Lab for IoT and Industry 4.03 a<p>The rapid adoption of the Internet of Things (IoT) across various industries, including manufacturing, healthcare, transportation, energy, and smart cities, has created a significant demand for skilled professionals in IoT hardware design, software development, deployment, and maintenance. However, the educational landscape has struggled to keep pace with this demand, as many universities lack dedicated IoT programs or fail to integrate IoT education into non-IT disciplines. This gap has led to a shortage of professionals skilled in IoT, posing a challenge for industries seeking to harness IoT technologies effectively. To address this challenge, next-generation remote laboratory infrastructures have emerged as a viable solution, enabling hands-on learning with IoT devices without the constraints of physical access to hardware. These infrastructures are particularly valuable for lifelong learners, students in remote areas, and institutions with limited resources, offering scalable and flexible access to IoT experimentation platforms via the Internet. Furthermore, the COVID-19 pandemic underscored the necessity of resilient and accessible online learning solutions, reinforcing the role of Virtual and Remote E-Laboratory (VREL) platforms in modern education. This paper presents the design, implementation, and operation of a next-generation remote laboratory infrastructure for IoT and Industry 4.0 education and research. Our approach builds upon the outcomes of two European Commission-funded projects—IOT-OPEN.EU (2016-2019) and IOT-OPEN.EU Reloaded (2022-2025) focuses on providing scalable, secure, and accessible remote IoT education solutions. We outline the architectural framework, key components, and technical challenges of deploying such infrastructures, including connectivity, security, scalability, and user management. Additionally, we discuss the educational impact of these platforms and their role in addressing the global IoT skills gap. Our findings demonstrate that remote IoT laboratories can be an effective and scalable alternative to traditional hands-on learning, supporting academic institutions and industry professionals in acquiring practical IoT skills. These infrastructures can enhance IoT education and workforce readiness by leveraging open-source technologies and modular architectures, ensuring broader accessibility and fostering innovation in the field.</p>
01024nas a2200145   4500000000100000000000100001008004100002260002700043100002000070700002500090700002500115700001900140245007400159520064500233       2025                            d  bElsevieraOsaka, Japan1 aPiotr Czekalski1 aMirosław Borowiecki1 aBolesław Pochopie´1 aGodlove Kuaban00aAn Analysis and Implementation of the Switching Sequence Table Method3 a<p><br />
This paper details the algorithm implementation of the Switching Sequence Table method in asynchronous sequential circuit Boolean switching system design. The algorithm is theoretically analysed in terms of its computational complexity and validated through actual experiments. It discusses the multidimensional problem of various parameters and their impact on performance. It then presents analytical evidence that reducing this multidimensional problem to a standard, one-dimensional problem is feasible. The authors consider method challenges when implementing computing modules for the Switching Sequence Table method.</p>
02493nas a2200145   4500000000100000000000100001008004100002260001700043100001500060700001900075700001700094700001900111245013600130520208100266       2025                            d  c 25-Nov-20251 aKinge Osee1 aValery Nkemeni1 aMichael Sone1 aGodlove Kuaban00aA Hybrid Ensemble-Based QoE Prediction from QoS and User Satisfaction Data: A case study on Cameroon&#x2019;s 3G/4G Mobile Networks3 a<p>The rapid growth of mobile internet and social media usage in Sub-Saharan Africa has amplified the need for accurate Quality of Experience (QoE) assessment in resource-constrained network environments. This paper introduces a novel hybrid Machine Learning (ML) framework for predicting user QoE in Cameroon’s Third Generation and Fourth Generation (3G/4G) networks, leveraging a unique real-world dataset that integrates crowdsourced Quality of Service (QoS) measurements with subjective user satisfaction surveys. Addressing the limitations of existing QoE studies that focus on well-resourced regions, our approach proposes <em>QoE Predictor (QoEPredict)</em>. This stacking ensemble combines eXtreme Gradient Boosting&nbsp;(XGBoost) and Random Forest classifiers with an XGBoost meta-learner. A key innovation is the use of disagreement features to capture divergences between base model predictions, allowing the meta-learner to resolve conflicts and enhance predictive accuracy.&nbsp;The proposed five-stage pipeline incorporates data preprocessing, feature engineering via Uniform Manifold Approximation and Projection (UMAP), unsupervised clustering, and Bayesian hyperparameter optimisation using Hyperopt, ensuring a robust and transferable methodology. Explainable AI (XAI) is integrated through SHapley Additive exPlanations (SHAP) analysis to provide feature-level interpretability and actionable insights for network operators. An experimental evaluation of 1,934 user sessions demonstrates that <em>QoEPredict</em>&nbsp;achieves a 90% F1 Score and accuracy, outperforming single-model baselines across all metrics.&nbsp;This work represents one of the first large-scale, interpretable QoE prediction frameworks for mobile social media applications in Sub-Saharan Africa. By combining ensemble modelling with explainability and contextualised insights, the study offers both methodological advances and practical guidance for implementing QoE-aware network management strategies in developing regions facing infrastructural and operational constraints.</p>
00447nas a2200133   4500000000100000000000100001008004100002100002800043700002200071700002700093700002000120245012300140856005000263       2024                            d1 aGustavo Machado-Guillen1 aJorge Cruz-Duarte1 aSantiago Conant-Pablos1 aKatarzyna Filus00aHarnessing Machine Learning for Reliable Weather Forecasting: Meteorological Impact on Sustainable Energy in Monterrey  uhttps://ieeexplore.ieee.org/document/1061199300417nas a2200121   4500000000100000000000100001008004100002100002600043700002000069700001900089245011500108856007200223       2024                            d1 aSebastian Pokuciński1 aKatarzyna Filus1 aDariusz Mrozek00aFinding the Perfect Cut: Selection of the Best Cutting Point in Equirectangular Panoramas for Object Detection  uhttps://www.sciencedirect.com/science/article/pii/S187705092402472400357nas a2200121   4500000000100000000000100001008004100002100002000043700002400063700002000087245010700107856002100214                                       d1 aKatarzyna Filus1 aMichał Romaszewski1 aMateusz Żarski00aSemantic Depth Matters: Explaining Errors of Deep Vision Networks through Perceived Class Similarities  uarXiv:2504.0995601423nas a2200121   4500000000100000000000100001008004100002100002000043700002000063245012200083856003700205520105900242                                       d1 aKatarzyna Filus1 aMateusz Żarski00aBeyond Accuracy: Uncovering the Role of Similarity Perception and its Alignment with Semantics in Supervised Learning  uhttps://arxiv.org/abs/2505.213383 a<p>Similarity manifests in various forms, including semantic similarity that is particularly important, serving as an approximation of human object categorization based on e.g. shared functionalities and evolutionary traits. It also offers practical advantages in computational modeling via lexical structures such as WordNet with constant and interpretable similarity. As in the domain of deep vision, there is still not enough focus on the phenomena regarding the similarity perception emergence. We introduce Deep Similarity Inspector (DSI) -- a systematic framework to inspect how deep vision networks develop their similarity perception and its alignment with semantic similarity. Our experiments show that both Convolutional Neural Networks' (CNNs) and Vision Transformers' (ViTs) develop a rich similarity perception during training with 3 phases (initial similarity surge, refinement, stabilization), with clear differences between CNNs and ViTs. Besides the gradual mistakes elimination, the mistakes refinement phenomenon can be observed.</p>
00396nas a2200121   4500000000100000000000100001008004100002260000900043100002000052700002600072245009300098856008300191       2025                            d  c20251 aKatarzyna Filus1 aSebastian Pokuciński00aSPHEREA: Synthetic dataset generation for equirectangular panoramas of home environments  uhttps://www.sciencedirect.com/science/article/pii/S0950705125017897?via%3Dihub00257nas a2200085   4500000000100000008004100001100002000042700002200062245008700084       2026                            d1 aKatarzyna Filus1 aJorge Cruz-Duarte00aTarget Selection for Adversarial Evaluation of Vision Models Using Language Models00258nas a2200085   4500000000100000008004100001100002000042700002000062245009000082       2026                            d1 aKatarzyna Filus1 aMateusz Żarski00aInspecting Training Dynamics of Similarity Development in Supervised Vision Networks 00259nas a2200085   4500000000100000008004100001100002000042700002600062245008500088       2026                            d1 aKatarzyna Filus1 aSebastian Pokuciński00aMeasuring Monosemanticity in Sparse Autoencoders via Latent Activation Coherence00310nas a2200109   4500000000100000008004100001100002000042700001600062700002000078700002200098245008000120       2026                            d1 aKatarzyna Filus1 aKamil Faber1 aRoberto Corizzo1 aChristopher Kanan00aLost or Hidden? A Concept-Level Forgetting in Supervised Continual Learning01716nas a2200169   4500000000100000000000100001008004100002260001200043100001500055700002200070700001900092245007800111856005800189490000600247520127900253022001401532       2021                            d  c10/20211 aMert Nakip1 aCüneyt Güzeliş1 aOsman Yıldız00aRecurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection  uhttps://ieeexplore.ieee.org/abstract/document/94515530 v93 a<p>We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall-τ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications.</p>
  a2169-353602529nas a2200181   4500000000100000000000100001008004100002260001200043100001900055700001500074700002200089700001700111245007400128856005800202490000600260520206700266022001402333       2020                            d  c11/20211 aVolkan Rodoplu1 aMert Nakip1 aRoozbeh Qorbanian1 aDeniz Eliiyi00aMulti-Channel Joint Forecasting-Scheduling for the Internet of Things  uhttps://ieeexplore.ieee.org/abstract/document/92602180 v83 a<p>We develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels, as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand, Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance, thus obviating contention, collision and handshaking, which are found in reactive protocols. In this paper, we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system, first, we design a heuristic, called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL), that solves the general form of the scheduling problem. Second, for the special case of identical channels, we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB), both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption, and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore, we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.</p>
  a2169-353601073nas a2200145   4500000000100000000000100001008004100002260002200043100001500065700001800080700002200098700001900120245015800139520063000297       2021                            d  bIEEEaNew Orleans1 aMert Nakip1 aAlperen Helva1 aCüneyt Güzeliş1 aVolkan Rodoplu00aSubspace-Based Emulation of the Relationship Between Forecasting Error and Network Performance in Joint Forecasting-Scheduling for the Internet of Things3 a<p>We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology, an Artificial Neural Network (ANN) learns this relationship while the forecasting error is kept inside a subspace of the entire space of forecasting errors during training. We apply our methodology to the case of Joint Forecasting-Scheduling (JFS) for the Internet of Things (IoT). Our results hold potential to improve the performance of JFS in next-generation networks and can be applied to a much wider range of problems beyond IoT.</p>
01907nas a2200145   4500000000100000000000100001008004100002100001500043700002300058700002200081700001900103245010600122856004900228520148400277       2021                            d1 aMert Nakip1 aKubilay Karakayali1 aCüneyt Güzeliş1 aVolkan Rodoplu00aAn End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things  uhttps://ieeexplore.ieee.org/document/94771833 a<p>We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and embedded feature selection methods, our architecture enables the automatic selection of features dynamically based on feature importance score calculation and gamma-gated feature selection units that are trained jointly and end-to-end with the forecaster. We compare the performance of our FSF architecture on the problem of forecasting IoT device traffic against the following existing (feature selection, forecasting) technique pairs: Autocorrelation Function (ACF), Analysis of Variance (ANOVA), Recurrent Feature Elimination (RFE) and Ridge Regression methods for feature selection, and Linear Regression, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), 1 Dimensional Convolutional Neural Network (1D CNN), Autoregressive Integrated Moving Average (ARIMA), and Logistic Regression for forecasting. We show that our FSF architecture achieves either the best or close to the best performance among all of the competing techniques by virtue of its dynamic, automatic feature selection capability. In addition, we demonstrate that both the training time and the execution time of FSF are reasonable for IoT applications. This work represents a milestone for the development of predictive networks for IoT in smart cities of the near future.</p>
01151nas a2200145   4500000000100000000000100001008004100002260000900043100001700052700001600069700001500085700001900100245008800119520079800207       2021                            d  bIEEE1 aErdem Çakan1 aAral Şahin1 aMert Nakip1 aVolkan Rodoplu00aMulti-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning3 a<p>We develop a Multi-Layer Perceptron (MLP) Decomposition<br />
architecture for mobile Internet Things (IoT) indoor<br />
positioning. We demonstrate the performance of our architecture<br />
on an indoor system that utilizes ultra-wideband (UWB) positioning.<br />
Our architecture outperforms the following benchmark<br />
processing techniques on the same data: MLP, Linear Regression,<br />
Ridge Regression, Support Vector Regression, and the Least<br />
Squares Method for indoor positioning. The results show that our<br />
architecture can significantly advance the positioning accuracy<br />
of indoor positioning systems and enable indoor applications<br />
such as navigation, proximity marketing, asset tracking, collision<br />
avoidance, and social distancing.</p>
01413nas a2200157   4500000000100000000000100001008004100002260000900043100001700052700001900069700001800088700001500106700001900121245010300140520101200243       2021                            d  bIEEE1 aAlper Saylam1 aRifat Cikmazel1 aNur Kelesoglu1 aMert Nakip1 aVolkan Rodoplu00aEnergy-Efficient Indoor Positioning for Mobile Internet of Things Based on Artificial Intelligence3 a<p>We develop an energy-efficient indoor positioning system based on Artificial Intelligence (AI). In our system, first, at the positioning layer, a Multi-Layer Perceptron (MLP) estimates the current indoor position of an IoT device based on positioning indicators obtained from the anchors. Second, at the forecasting layer, a pair of MLPs estimate the future positions of the device based on the past position estimates obtained when the device woke up as well as the forecast positions of the device during the sleep periods. Third, the device is awakened to send a positioning beacon at intervals over which a significant displacement is predicted to occur by the forecasting layer. Our results demonstrate that our indoor positioning system saves significant energy via adaptive sleep cycles whose duration is determined by the &nbsp;prediction of a significant displacement. This work establishes a foundation for indoor positioning that utilizes AI-based positioning and trajectory forecasting.</p>
01815nas a2200133   4500000000100000000000100001008004100002260000900043100001500052700001600067700002200083245007600105520150000181       2021                            d  bIEEE1 aMert Nakip1 aOnur Çopur1 aCüneyt Güzeliş00aComparative Study of Forecasting Models for COVID-19 Outbreak in Turkey3 a<p>This paper gives an explanation for the failure of machine learning models for the prediction of the cases and the other future trends of Covid-19 pandemic. The paper shows that simple Linear Regression models provide high prediction accuracy values reliably but only for a 2-weeks period and that relatively complex machine learning models, which have the potential of learning long-term predictions with low errors, cannot achieve to obtain good predictions with possessing a high generalization ability. It is suggested in the paper that the lack of a sufficient number of samples is the source of the low prediction performance of the forecasting models. To exploit the information, which is of most relevant with the active cases, we perform feature selection over a variety of variables such as the numbers of active cases, deaths, recoveries, and population. Furthermore, we compare Linear Regression, Multi-Layer Perceptron, and Long-Short Term Memory models each of which is used for prediction of active cases together with various feature selection methods. Our results show that the accurate forecasting of the active cases with high generalization ability is possible up to 3 days because of the small sample size of COVID-19 data. We observe that the Linear Regression model has much better prediction performance with high generalization ability as compared to the complex models but, as expected, its performance decays sharply for more than 14-days prediction horizons.</p>
01615nas a2200157   4500000000100000000000100001008004100002260002700043100001700070700001800087700001900105700001500124700001900139245014200158520115700300       2021                            d  bIEEEaIstanbul, Turkey1 aAlper Saylam1 aNur Kelesoglu1 aRifat Cikmazel1 aMert Nakip1 aVolkan Rodoplu00aDynamic Positioning Interval Based On Reciprocal Forecasting Error (DPI-RFE) Algorithm for Energy-Efficient Mobile IoT Indoor Positioning3 a<p>We develop an algorithm called “Dynamic Positioning Interval based on Reciprocal Forecasting Error (DPIRFE)” for energy-efficient mobile Internet of Things (IoT) Indoor Positioning (IP). In contrast with existing IP algorithms, DPIRFE forecasts the future trajectory of a mobile IoT device by using machine learning and dynamically adjusts the positioning interval based on the reciprocal instantaneous forecasting error, thereby dynamically trading off transmit energy consumption against forecasting error. We compare the performance of DPIRFE with respect to the total transmit energy consumption and the average forecasting error against Constant Positioning Interval (CPI) and Positioning Interval based on Displacement (PID) algorithms. Our results show that DPI-RFE significantly outperforms both of these benchmark algorithms with respect to transmit energy consumption while achieving a competitive average forecasting error performance. These results open the way to the design of machine learning based trajectory forecasting algorithms that can be utilized for energy-efficient positioning in next-generation wireless networks.</p>
01330nas a2200145   4500000000100000000000100001008004100002260002400043100001500067700001400082700002300096700002200119245012500141520091800266       2021                            d  bIEEEaBursa, Turkey1 aMert Nakip1 aArda Asut1 aCennet Kocabıyık1 aCüneyt Güzeliş00aA Smart Home Demand Response System based on Artificial Neural Networks Augmented with Constraint Satisfaction Heuristic3 a<p>Distributing the peak load and alleviating grid stress by considering hourly electricity prices are some of the main research problems for current smart grid systems. This paper deals with the scheduling problem of home appliances' operating hours in smart grids, which aims to achieve minimum cost in user-defined operation intervals. To this end, scheduling via Artificial Neural Networks Augmented with Constraint Satisfaction Heuristic (ANN-AH) method that emulates the operation of the optimization for smart home demand response is developed. Our results show that a home demand response via ANN-AH achieves close to optimal performance with 10 times lower execution time than the optimal scheduling. These results suggest that the ANN-AH based demand response is highly successful and practical, and it is promising for future applications in micro-grid and decentralized renewable energy systems.</p>
02131nas a2200133   4500000000100000000000100001008004100002100001500043700001700058700001900075700002200094245014100116520174000257       2022                            d1 aMert Nakip1 aErdem Çakan1 aVolkan Rodoplu1 aCüneyt Güzeliş00aDynamic Automatic Forecaster Selection via Artificial Neural Network Based Emulation to Enable Massive Access for the Internet of Things3 a<p>The Massive Access Problem of the Internet of Things (IoT) occurs at the uplink Medium Access Control (MAC) layer when a massive number of IoT devices seek to transfer their data to an IoT gateway. Although recently proposed predictive access solutions that schedule the uplink traffic based on forecasts of IoT device traffic achieve high network performance, these solutions depend heavily on the performance of forecasters. Hence, the design and selection of forecasting schemes are key to enabling massive access for such predictive access solutions. To this end, in this paper, first, we develop a framework that emulates the relationship between the IoT device class composition in the coverage area of an IoT gateway and the resulting network performance by virtue of an Artificial Neural Network (ANN). Second, based on this framework, we develop the Dynamic Automatic Forecaster Selection (DAFS) method, which selects the best-performing forecasting scheme for predictive access, in particular for &nbsp;Joint Forecasting-Scheduling (JFS), in a manner that adapts dynamically to a changing number of IoT devices in each device class in the coverage area. We evaluate the performance of DAFS via simulations and show that our method is able to achieve at least $80\%$ of the best performance that can be attained for both throughput and energy consumption. Furthermore, we demonstrate that DAFS is robust with respect to the selection of architectural parameters and has a reasonable computation time for real-time IoT applications. These results imply that DAFS holds the potential for practical implementation at IoT gateways in order to enable massive access under a dynamically changing composition of IoT devices.</p>
01242nas a2200145   4500000000100000000000100001008004100002260001300043100001600056700001500072700002200087700002000109245007700129520089000206       2022                            d  bSpringer1 aOnur Çopur1 aMert Nakip1 aSimone Scardapane1 aJürgen Slowack00aEngagement Detection with Multi-Task Training in E-Learning Environments3 a<p>Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user experience and efficiency by providing valuable feedback. In this paper, we propose a novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes mean squared error and triplet loss together to determine the engagement level of students in an e-learning environment. The performance of this system is evaluated and compared against the state-ofthe-art on a publicly available dataset as well as videos collected from real-life scenarios. The results show that ED-MTT achieves 6% lower MSE than the best state-of-the-art performance with highly acceptable training time and lightweight feature extraction.</p>
02575nas a2200169   4500000000100000000000100001008004100002260001200043100001500055700001500070700001900085700002200104245012100126490000700247520213700254022001402391       2022                            d  c05/20221 aMert Nakip1 aBaran Gül1 aVolkan Rodoplu1 aCüneyt Güzeliş00aPredictability of Internet of Things Traffic at the Medium Access Control Layer Against Information-Theoretic Bounds0 v103 a<p>Most of the existing Medium Access Control (MAC) layer protocols for the Internet of Things (IoT) model the traffic generated by each IoT device via random arrivals such as those in a Poisson process. Under this model, since it is implied that IoT device traffic cannot be predicted, only reactive MAC-layer protocols in which the network responds to the current traffic are viable. In contrast, recent work has demonstrated that the traffic generated by an individual IoT device can be predictable, thus enabling predictive network protocols at the MAC layer. In this paper, we investigate information-theoretic bounds on the predictability of IoT traffic of individual devices. To this end, first, we compare the performance achieved by the following state-of-the-art forecasters on individual IoT device traffic: Logistic Regression, Multi-Layer Perceptron (MLP), 1-Dimensional Convolutional Neural Network (1D CNN), and Long Short Term Memory (LSTM) as well as MLP under feature selection based on Analysis of Variance (ANOVA) and Auto-Correlation Function (ACF). Second, we quantify the gap between the performance of these forecasters against information-theoretic bounds as follows: For IoT devices that generate a fixed number of bits at each generation instance, we measure the gap between the forecasting accuracy and the information-theoretic bound established by Fano’s inequality on the probability of correct prediction. Our empirical results show that existing forecasting schemes perform close to the information-theoretic bound in this case. For IoT devices that generate a variable number of bits, we measure the gap between the Mean Square Error (MSE) and the estimation-theoretic counterpart to Fano’s inequality. Our empirical results show that the performance of existing forecasting schemes is far from the information-theoretic bound in this case. This work motivates the machine learning community to develop forecasting schemes that approach information-theoretic bounds. Furthermore, this work is expected to impact the development of predictive MAC-layer protocols that exploit these bounds.</p>
  a2169-353602279nas a2200181   4500000000100000000000100001008004100002260001200043100001500055700001900070700001500089700001800104700002200122245015600144490000700300520177600307022001402083       2022                            d  c06/20221 aArif Dayı1 aVolkan Rodoplu1 aMert Nakip1 aBuse Pehlivan1 aCüneyt Güzeliş00aMulti-Channel Subset Iteration with Minimal Loss in Available Capacity (MC-SIMLAC) Algorithm for Joint Forecasting-Scheduling in the Internet of Things0 v133 a<p>The Massive Access Problem of the Internet of Things (IoT) refers to the problem of scheduling the uplink transmissions of a massive number of IoT devices in the coverage area of an IoT gateway. Joint Forecasting-Scheduling (JFS) is a recently developed methodology in which an IoT gateway forms predictions of the future uplink traffic generation pattern of each IoT device in its coverage area via machine learning algorithms and uses these predictions to schedule the uplink traffic of all of the IoT devices in advance. In this paper, we develop a novel algorithm, which we call “Multi-Channel Subset Iteration with Minimal Loss in Available Capacity” (MC-SIMLAC), for multi-channel joint forecasting-scheduling. Our multi-channel scheduling algorithm iterates over subsets of all of the bursts of IoT device traffic and selects channel-slot pairs by targeting the minimization of loss in total available capacity. In this regard, our algorithm contrasts sharply with Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL), which is the best-performing heuristic that has been developed so far for multi-channel JFS. In the general case, our algorithm outperforms MC-LAPAL, especially when wireless links operate in the power-limited regime and the number of devices is large. For the special case of identical channels, our algorithm achieves a performance that is closer than MC-LAPAL to that of the optimal scheduler. Furthermore, we prove that the time complexity and the space complexity of MC-SIMLAC in the worst case are polynomial in each of the system parameters, which indicates practical feasibility. These results pave the way to the widespread use of multi-channel joint forecasting-scheduling at IoT gateways in the near future.</p>
  a2093-537401805nas a2200157   4500000000100000000000100001008004100002260001200043100001500055700001800070700002200088700001900110245009400129856004900223520137500272       2022                            d  c11/20221 aMert Nakip1 aAlperen Helva1 aCüneyt Güzeliş1 aVolkan Rodoplu00aMOSAL: A Subspace Based Forecasting Algorithm for Throughput Maximization in IoT Networks  uhttps://ieeexplore.ieee.org/document/99429293 a<p>Predictive solution techniques have been developed recently in order to solve the Massive Access Problem of the Internet of Things (IoT). These techniques forecast the traffic generation patterns of individual IoT devices in the coverage area of an IoT gateway and schedule the MAC-layer resources at the gateway in advance based on these forecasts. Although predictive solutions have achieved high network performance, a key problem is that their performance depends highly on the performance of forecasters. In this paper, in order to minimize the effects of forecasting errors on the performance of predictive networks, we develop a subspace based forecasting algorithm called “Motion On a Subspace under Adaptive Learning rate (MOSAL)”. First, our algorithm trains a forecaster by minimizing the performance loss of an IoT network based on the emulation of an Application-Specific Error Function by an Artificial Neural Network. Second, the algorithm moves close to a subspace of the forecasting errors while it aims to maximize network throughput. Our results show that MOSAL achieves a throughput performance that surpasses the performance of commonly used standard gradient descent training algorithms at a reasonable execution time. These results open the way to the deployment of predictive solutions at IoT Gateways in practice in the near future.</p>
01672nas a2200181   4500000000100000000000100001008004100002260001200043100001500055700001600070700001600086700002200102245014200124856005000266490000800316520115200324022001401476       2023                            d  c03/20231 aMert Nakip1 aOnur Çopur1 aEmrah Biyik1 aCüneyt Güzeliş00aRenewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network  uhttps://authors.elsevier.com/a/1gr4J15eif8Fs50 v3403 a<p>Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques.</p>
  a0306-261901999nas a2200121   4500000000100000000000100001008004100002100001800043700001500061700002200076245009000098520168900188       2024                            d1 aPervin Bulucu1 aMert Nakip1 aCüneyt Güzeliş00aMulti-Sensor E-Nose based on Online Transfer Learning Trend Predictive Neural Network3 a<p>Electronic Nose (E-Nose) systems, widely applied across diverse fields, have revolutionized quality control, disease diagnostics, and environmental management through their odor detection and analysis capabilities. The decision and analysis of E-Nose systems often enabled by Machine Learning (ML) models that are trained offline using existing datasets. However, despite their potential, offline training efforts often prove intensive and may still fall short in achieving high generalization ability and specialization for considered application. To address these challenges, this paper introduces the e-rTPNN decision system, which leverages the Recurrent Trend Predictive Neural Network (rTPNN) combined with online transfer learning. The recurrent architecture of the e-rTPNN system effectively captures temporal dependencies and hidden sequential patterns within E-Nose sensor data, enabling accurate estimation of trends and levels. Notably, the system demonstrates the ability to adapt quickly to new data during online operation, requiring only a small offline dataset for initial learning. We evaluate the performance of the e-rTPNN decision system in two domains: beverage quality assessment and medical diagnosis, using publicly available wine quality and Chronic Obstructive Pulmonary Disease (COPD) datasets, respectively. Our evaluation indicates that the proposed e-rTPNN achieves decision accuracy exceeding 97% while maintaining low execution times. Furthermore, comparative analysis against established Machine Learning (ML) models reveals that the e-rTPNN decision system consistently outperforms these models by a significant margin in terms of accuracy.</p>
02003nas a2200145   4500000000100000000000100001008004100002260001200043100001500055700001800070700002200088245012400110490000800234520161500242       2024                            d  c10/20241 aMert Nakip1 aNur Kelesoglu1 aCüneyt Güzeliş00aFire Detection and Risk Assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization0 v1643 a<p>We propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments&nbsp;l2&nbsp;norm based smoothing term into an&nbsp;l1-l2&nbsp;combination, facilitating the integration of&nbsp;l1&nbsp;regularization into the SVR method, thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires, benchmarking against 15 machine learning models, including state-of-the-art techniques, such as Recurrent Trend Predictive Neural Network (rTPNN), Long-Short Term Memory (LSTM), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire, enabling early preventive measures. Notably, it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly, it exhibits computation times significantly lower – by 1 to 2 orders of magnitude – than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR, facilitated by flattening-samples based augmented regularization, allows for high detection performance even with smaller training sets.</p>
01337nas a2200121   4500000000100000000000100001008004100002260008100043100001500124245010800139856004500247520092300292       2024                            d  c11/2024bKorean Institute of Next Generation ComputingaPhilippines (Hybrid)1 aMert Nakip00aAnalysis of the QoS Prediction via Recurrent Trend Predictive Neural Network under Low-Rate DoS Attacks  uhttps://www.earticle.net/Article/A4688843 a<p>Low-Rate Denial of Service (LDoS) attacks raise an increasingly frequent and significant threat to performancecritical and sensitive networks. Due to their slowly evolving nature, it is challenging –but crucial– to detect such attacks during their early phases in order to mitigate their impact on network performance, e.g. Quality of Service (QoS), in longterm operation. To this end, this paper investigates the prediction of QoS via a modified version of the Recurrent Trend Predictive Neural Network (rTPNN) and the use of the prediction towards detracting LDoS attacks. The presented rTPNN-based QoS predictor is evaluated and compared against benchmark models for five scenarios using an open-access dataset. The results have shown that the modified rTPNN model can predict QoS with under 2% SMAPE, and the QoS prediction is a promising approach for developing LDoS attack detectors in future works.</p>
00541nas a2200205   4500000000100000008004100001260001200042653001900054653002000073653002000093653002200113653001700135100001500152700001800167700001600185700002200201245009700223300000700320490000800327       2026                            d  c07/202610aFire detection10aSelf-Supervised10aonline learning10ahuman-in-the-loop10amulti-sensor1 aMert Nakip1 aNur Kelesoglu1 aTaylan Mert1 aCüneyt Güzeliş00aSelf-Supervised Online Learning Algorithm for rTPNN to Reduce False Alarms in Fire Detection  a140 v35001379nas a2200097   4500000000100000008004100001100001500042245009000057856007200147520106200219       2026                            d1 aMert Nakip00aA predictive neural network architecture for early detection of low-rate cyberattacks  uhttps://www.sciencedirect.com/science/article/pii/S09507051260072153 a<p>Low-Rate Denial of Service (LDoS) attacks pose a significant challenge to IoT networks due to their subtle and prolonged nature, often evading traditional intrusion detection systems. This paper presents IDQS (Intrusion Detection via QoS Prediction), a lightweight and proactive framework for early LDoS attack detection. IDQS integrates two new key components: (i) RTP-QoS, a Recurrent Trend Predictive Neural Network that learns and forecasts future Quality of Service (QoS) based on historical traffic patterns, and (ii) PDM, a Pairwise Decision Model that evaluates discrepancies between predicted and actual QoS to identify potential attacks. Evaluated on the public SDN-SlowRate-DDoS and CIC-IDS2017 datasets, IDQS respectively achieves over 79% and 91% detection accuracy across most attack scenarios with high recall and low false negatives, while maintaining an end-to-end inference time of just 0.28 seconds. The results demonstrate the effectiveness and efficiency of IDQS for real-time deployment in resource-constrained IoT environments.</p>01808nas a2200121   4500000000100000008004100001260003100042100001500073700002000088245010100108856005900209520141800268       2026                            d  bIEEEaThessaloniki, Greece1 aMert Nakip1 aMikołaj Macura00aPerformance, Energy Efficiency, and Security of Distributed rTPNN for TinyML-Enabled Smart Homes  uhttps://ieeexplore.ieee.org/abstract/document/115946673 a<p><span style="-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);color:rgb(34, 34, 34);display:inline !important;float:none;font-family:Arial, sans-serif;font-size:13px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;">Tiny Machine Learning (TinyML) brings AI capabilities to low-power edge devices, enabling local data processing without cloud reliance. This paper evaluates a distributed Recurrent Trend Predictive Neural Network (rTPNN) deployed on ESP8266 and ESP32 microcontrollers within a smart home IoT network. The rTPNN achieves superior accuracy with fewer parameters than standard models while incurring negligible computational costs. Energy profiling demonstrates that constant WiFi connectivity extends battery life eight-fold compared to periodic reconnection. However, security assessments reveal that constant connectivity is highly susceptible to Denial of Service (DoS) saturation, and parameter injection can degrade accuracy by over 70%. These results substantiate the feasibility of rTPNN as a TinyML for sustained operation while highlighting the critical need for codesigned security mechanisms.</span></p>03297nas a2200145   4500000000100000008004100001100001500042700001800057700001900075700002200094245010900116856005900225300000700284520286000291       2026                            d1 aMert Nakip1 aPervin Bulucu1 aOsman Yıldız1 aCüneyt Güzeliş00aTrends and Multidimensional Analysis of Performance-Influencing Components in Electronic Noses: A Survey  uhttps://ieeexplore.ieee.org/abstract/document/11599553  a273 a<div class="abstract-text row g-0" style="--bs-gutter-x:0;--bs-gutter-y:0;-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);box-sizing:border-box;clear:both;color:rgb(51, 51, 51);display:flex;flex-wrap:wrap;font-family:&quot;HelveticaNeue Regular&quot;, sans-serif;font-size:18px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;margin:0px 0px 1em !important;orphans:2;outline:none !important;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;" _ngcontent-ng-c1231153542=""><div class="col-12" style="box-sizing:border-box;flex:0 0 auto;margin-top:0px;max-width:100%;outline:none !important;padding-left:0px;padding-right:0px;width:886.963px;" _ngcontent-ng-c1231153542=""><div class="u-mb-1" style="box-sizing:border-box;margin-bottom:1em !important;outline:none !important;" _ngcontent-ng-c1231153542=""><div class="abstract-text-content" style="box-sizing:border-box;outline:none !important;" _ngcontent-ng-c1231153542="" xplmathjax="">Electronic Nose (E-Nose) systems have emerged as powerful analytical tools engineered to mimic the mammalian olfactory system, offering rapid, non-invasive, and cost-effective alternatives to traditional chemical analysis methods. Given the vast and multifaceted nature of recent research, this paper presents a detailed and multidimensional analysis of the primary components dictating E-Nose efficacy: target analytes, gas sensors, and Machine Learning (ML) algorithms. Based on a structured review of literature published between 2019 and 2025, we quantitatively explore the interdependencies among these components to identify key trends and performance drivers. Our statistical analysis reveals a continued reliance on Metal Oxide Semiconductor (MOS) sensors due to their cost-effectiveness, alongside a significant paradigm shift from traditional statistical methods toward Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNNs) and hybrid models, which are increasingly utilized to handle high-dimensional sensor data. Furthermore, we provide a granular review of applications across major sectors, including Food and Beverage, covering quality assessment, fraud detection, and flavor characterization, as well as Healthcare, and Environmental and Agricultural Monitoring. Finally, this survey critically evaluates persistent challenges such as sensor drift, detection performance, data scarcity, and explainability, while outlining emerging research opportunities in novel nanomaterials, IoT integration, Edge Computing, and multi-modal sensing to facilitate the transition from laboratory prototypes to robust commercial applications.</div></div></div></div>02012nas a2200121   4500000000100000008004100001100001500042700001500057700001700072700002000089245008700109520169400196       2026                            d1 aBaran Gül1 aMert Nakip1 aNasser Jazdi1 aMichael Weyrich00aQoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles3 a<p>Modern smart vehicles leverage multimodal sensors, ranging from high-bandwidth vision systems to low-rate physiological monitors, to provide personalized in-cabin services. However, integrating high-fidelity multimodal fusion with collaborative training is often hindered by the heterogeneous and time-varying Quality of Service (QoS) constraints of vehicular networks. Standard Federated Learning (FL) approaches enforce rigid synchronous rounds that fail to account for these resource asymmetries, leading to safety-critical timing violations and energy exhaustion. In this paper, we propose FedQoS, a novel asynchronous, event-triggered FL framework that decouples local computation from global communication via a two-phase gating mechanism. First, we introduce a resource-aware training gate that initializes local learning only when sensing buffers and energy reserves meet safety thresholds, preventing ML tasks from compromising core vehicle mobility. Second, a QoS-aware transmission policy gates uplink updates based on an efficiency score that balances model novelty against instantaneous latency and energy costs. Locally, clients optimize an objective featuring a staleness-aware proximal term that dynamically adjusts the global anchor strength based on update age. Extensive experiments on multimodal vehicular datasets demonstrate that FedQoS achieves competitive personalized accuracy with only marginal performance loss compared to FedAvg, while substantially reducing QoS violations, cutting communication overhead by 76.7%, and lowering latency cost by 26.0%, demonstrating a highly favorable accuracy and efficiency balance for real-world vehicular deployments.</p>02527nas a2200097   4500000000100000008004100001100001500042700001900057245008300076520227000159       2026                            d1 aMert Nakip1 aRafał Gibała00aAn LLM-Guided Agentic Optimization Toolbox for Edge-Deployable Neural Networks3 a<p><span style="-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);color:rgb(0, 0, 0);display:inline !important;float:none;font-family:&quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serif;font-size:12px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:left;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;">Deploying neural networks on resource-constrained edge devices is limited by severe envelopes on storage, memory, compute, and energy. Manual compression through pruning, weight clustering, and quantization can reduce footprint substantially, yet selecting an appropriate strategy and its hyperparameters remains a search-heavy process. In this paper, we present an automated Edge AI Optimization Toolbox that closes the loop between weight-distribution profiling, user-defined multi-objective preferences, and Large Language Model (LLM)-guided strategy synthesis for Neural Networks targeting edge export. Given a pretrained model, a calibration and evaluation dataset, and constraints on minimum accuracy together with maximum model size and RAM, the toolbox profiles layer-wise weight statistics, proposes a compression strategy among pruning, clustering, or their combination, always applies int8 quantization, re-evaluates the candidate, and iterates until the preferences are met or a maximum iteration budget is exhausted, returning either an ideal or closest-matching model. The approach is aligned with Green AI goals of reducing unnecessary computational and memory footprints at deployment time. On occupancy estimation models spanning MLP, CNN, LSTM, and public pretrained architectures, the toolbox reduces model size by roughly 72%-92% while largely preserving predictive quality. Inference latency significantly improves for several models, whereas energy and carbon impacts remain architecture-dependent under the current operator scope. These results show that preference-constrained, LLM-guided compression is an effective and promising path toward edge-deployable and resource-aware neural networks.</span></p>02289nas a2200121   4500000000100000008004100001100001500042700001500057700001700072700002000089245008700109520197100196       2026                            d1 aBaran Gül1 aMert Nakip1 aNasser Jazdi1 aMichael Weyrich00aEnergy- and QoS-Gated Federated Learning for Sustainable Wearable Stress Detection3 a<p>Federated Learning (FL) is increasingly deployed on battery-powered edge and wearable devices, where every communication round and local training step carries a direct energy and carbon cost. Most FL evaluations, however, still report predictive accuracy alone, leaving the environmental footprint of the learning process itself unmeasured. This paper presents a two-phase, QoS- and energy-gated FL approach, FedEQ, which restricts local training to moments when a client's sensing buffer and residual energy meet safety thresholds, and restricts transmission to updates whose novelty justifies their instantaneous latency and energy cost. We evaluate FedEQ on a federated stress-detection task built from the public WESAD dataset, benchmarking it against three established aggregation schemes: FedAvg, FedProx, and the adaptive server-side optimizer FedAdam. Beyond accuracy and F1, we report communication volume, wall-clock-measured compute energy (via CodeCarbon), analytically modeled communication energy, CO2eq emissions, and floating-point operations, averaged over five random seeds. FedAdam attains the highest raw accuracy of the four methods (93.2%) at the shortest horizon we test (20 rounds); FedEQ retains 97.0% of that accuracy at the same horizon while cutting communication volume by 63.9%, total energy and CO2eq by 68.9-69.7%, and FLOPs by 78.6% relative to all three baselines, for 2.7-3.2x higher accuracy-per-resource-unit (per MB, per Wh, and per gCO2eq) than the strongest competing method. We further show this accuracy gap is a short-horizon artifact rather than a fixed property of the method: extending training to 60 rounds closes it to within 0.5 points of the best baseline - and FedEQ overtakes FedAdam outright - while its communication savings simultaneously grow to 69.1%. Across the full range tested, FedEQ is the Pareto-dominant choice among the four once predictive performance and resource cost are considered jointly.</p>00525nas a2200181   4500000000100000000000100001008004100002260002600043100001900069700002000088700001800108700001900126700002000145700001900165700001800184700001700202245012400219       2022                            d  c12/2022aOsaka, Japan1 aJakub Szyguła1 aPiotr Biernacki1 aDariusz Marek1 aAdam Domański1 aŁukasz Sobczak1 aJarosław Flak1 aDariusz Caban1 aPiotr Pawlas00aAnalysis of web-based geo-visualization methods applied for Automated Guided Vehicle using Satellite Navigation Systems00367nas a2200133   4500000000100000000000100001008004100002260001200043100001800055700002000073245008600093856004700179490000700226       2024                            d  c11/20241 aNur Kelesoglu1 aŁukasz Sobczak00aZigBeeNet: Decrypted ZigBee IoT Network Traffic Dataset in Smart Home Environment  uhttps://www.mdpi.com/2076-3417/14/23/108440 v1402216nas a2200169   4500000000100000008004100001100001700042700002500059700002000084700002400104700002300128700001800151700001600169700001900185245007600204520176600280       2026                            d1 aAygün Varol1 aKatarzyna Kołodziej1 aŁukasz Sobczak1 aMichał Romaszewski1 aPrzemysław Głomb1 aNaser Motlagh1 aMirka Leino1 aJohanna Virkki00aEnabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing3 a<p>Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy--latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representations: raw sensor values, threshold-aware descriptions, and compact environmental summary flags. The approach is evaluated using indoor Raspberry Pi/BME680 datasets from Tampere University and outdoor air-quality datasets from Helsinki, Katowice, and Warsaw. We construct a binary LLM query dataset covering air quality, thermal comfort, and joint environmental conditions, and evaluate five local and five cloud LLMs across three prompt variants and two inference modes, with and without chain-of-thought prompting. The results show that prompt enrichment substantially improves local-model accuracy. In No-CoT mode, local accuracy increases from 50.9\% to 81.7\% indoors and from 63.7\% to 89.3\% outdoors from the raw to the most enriched prompt. Local No-CoT inference is the fastest configuration, with mean latency close to 0.22 s, while CoT substantially increases inference time. These findings suggest that lightweight prompt-side preprocessing can narrow the local--cloud performance gap and support low-latency IoT analytics in smart environments.</p>02266nas a2200181   4500000000100000008004100001100001700042700002500059700002000084700002400104700002300128700001800151700001600169700001900185245007600204856003800280520176600318       2026                            d1 aAygün Varol1 aKatarzyna Kołodziej1 aŁukasz Sobczak1 aMichał Romaszewski1 aPrzemysław Głomb1 aNaser Motlagh1 aMirka Leino1 aJohanna Virkki00aEnabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing  uhttps://arxiv.org/abs/2606.22496 3 a<p>Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy--latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representations: raw sensor values, threshold-aware descriptions, and compact environmental summary flags. The approach is evaluated using indoor Raspberry Pi/BME680 datasets from Tampere University and outdoor air-quality datasets from Helsinki, Katowice, and Warsaw. We construct a binary LLM query dataset covering air quality, thermal comfort, and joint environmental conditions, and evaluate five local and five cloud LLMs across three prompt variants and two inference modes, with and without chain-of-thought prompting. The results show that prompt enrichment substantially improves local-model accuracy. In No-CoT mode, local accuracy increases from 50.9\% to 81.7\% indoors and from 63.7\% to 89.3\% outdoors from the raw to the most enriched prompt. Local No-CoT inference is the fastest configuration, with mean latency close to 0.22 s, while CoT substantially increases inference time. These findings suggest that lightweight prompt-side preprocessing can narrow the local--cloud performance gap and support low-latency IoT analytics in smart environments.</p>03010nas a2200181   4500000000100000008004100001100001700042700002500059700002000084700002400104700002300128700001800151700001600169700001900185245011600204856005200320520245600372       2026                            d1 aAygün Varol1 aKatarzyna Kołodziej1 aŁukasz Sobczak1 aMichał Romaszewski1 aPrzemysław Głomb1 aNaser Motlagh1 aMirka Leino1 aJohanna Virkki00aAIRWISE: Environmental Sensor and LLM Query Dataset for Edge AI-Based Air-Quality and Thermal-Comfort Analytics  uhttps://zenodo.org/doi/10.5281/zenodo.20783874 3 a<p>AIRWISE is an open benchmark dataset for edge AI, IoT environmental monitoring, and large language model (LLM) evaluation on real sensor data. It combines indoor air-quality and thermal-comfort measurements, outdoor air-quality and meteorological time series, and a labeled binary question-answering benchmark for threshold-aware reasoning and anomaly detection. The dataset supports the study “Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing”:&nbsp;https://doi.org/10.48550/arXiv.2606.22496<br>AIRWISE provides three core resources:<br>Indoor environmental sensor dataset (BME680, 1-minute resolution). 146,000+ minute-averaged readings from Raspberry Pi + Bosch BME680 nodes in three real micro-environments (office, kitchen, hallway) at Tampere, Finland (2025-11-14 to 2025-12-18): temperature, relative humidity, barometric pressure, gas resistance, indoor air quality (IAQ) proxy, z-score anomaly detectors, and limit-annotated long-format tables with ground-truth anomaly flags.<br>Outdoor air-quality and meteorological time series (hourly, full-year 2023). Helsinki (Finland), Katowice and Warsaw (Poland): NO₂, CO, O₃, PM₂.₅, temperature, and relative humidity assembled from national monitoring networks (FMI, GIOŚ, IMGW-PIB), as cleaned single-series city tables (≈8,760 hourly records per city) and limit-annotated long-format tables with anomaly flags.<br>Binary LLM query benchmark (1,440 labeled yes/no questions). 240 natural-language questions per site across six sites, balanced by task type (80 air quality, 80 thermal comfort, 80 joint environmental condition), each with ground-truth answer, violation label, offending factors, and rationale — ready for benchmarking local/edge and cloud LLMs on sensor-grounded reasoning.<br>Also included: reference air-quality and thermal-comfort limits (EU air-quality standards and Finnish/Polish national guidelines); the unmodified institutional source files for the outdoor component (FMI exports, GIOŚ archive workbooks and station metadata, IMGW-PIB synoptic tables); the complete dataset-generation scripts covering every pipeline step (raw sources → cleaned tables → limit-annotated data → LLM queries) with a requirements.txt for a verified minimal Python environment; SHA-256 checksums; and a dependency-free validation script. Schemas, exact row counts, coverage, and usage examples are documented in README.md and MANIFEST.md.</p>00288nas a2200097   4500000000100000008004100001260001600042100001800058700002000076245009400096       2026                            d  aBari, Italy1 aNur Kelesoglu1 aŁukasz Sobczak00aWhy the Robot Said No: Knowledge-Graph-Grounded Explanations for LLM-Based Robot Planning02261nas a2200169   4500000000100000000000100001008004100002260001200043100001800055700001900073700001700092245007800109856005000187490000700237520183300244022001402077       2025                            d  c07/20251 aNur Kelesoglu1 aMarzena Halama1 aAnna Strzoda00aEnhancing LoRa-based Outdoor Localization Accuracy Using Machine Learning  uhttps://ieeexplore.ieee.org/document/110800100 v133 a<p>The Internet of Things is gaining significant relevance, driving increasing interest in location-<br />
based services using wireless signals, particularly Low Power Wide Area Network (LPWAN) technology.<br />
LoRa (Long Range), together with LoRaWAN, is a prominent LPWAN standard that provides long-range<br />
connectivity and low energy consumption, making it viable for IoT-based positioning systems in smart<br />
cities. For localization systems leveraging LoRa signals, Machine Learning (ML) approaches are being<br />
increasingly explored, as ML-based solutions offer a powerful way to enhance the accuracy of positioning.<br />
In this study, we propose various ML approaches for LoRa-based positioning in outdoor environments. We<br />
evaluate six different ML models: k-NN, CNN, SVR, ANN, XG-Boost, and LightGBM-using an open-<br />
source urban LoRaWAN dataset. We further propose a Hybrid Model that combines convolutional feature<br />
extraction with gradient-boosted regression. This architecture integrates the strengths of deep learning and<br />
tree-based models, aiming to capture both temporal signal patterns and structured input correlations for<br />
improved localization accuracy. The models are trained offline and tested for performance in terms of<br />
localization accuracy, mean square error, and computational efficiency. Additionally, we investigate the<br />
impact of different Feature Vector (FV) subsets on localization performance by analyzing the significance of<br />
LoRaWAN signal attributes. Our results highlight the effectiveness of ML models in enhancing localization<br />
accuracy for LoRa-based outdoor positioning systems, demonstrating performance improvements ranging<br />
from 10% to 73% compared to previous ML studies in outdoor localization.</p>
  a2169-353600341nas a2200097   4500000000100000008004100001260003000042100001800072245010200090856005100192       2026                            d  c10/05/2026aNaples, Italy1 aNur Kelesoglu00aJudging Turn-Taking Through a Monocultural Lens: A Parallel-Corpus Probe of Cultural Bias in LLMs  uhttps://dl.acm.org/doi/10.1145/3776591.383664700408nas a2200157   4500000000100000000000100001008004100002260001200043100001900055700003200074700001900106700002000125700001600145245008300161490000600244       2021                            d  c06/20211 aWojciech Kempa1 aJoanna Rydarowska-Kurzbauer1 aMarzena Halama1 aElżbieta Smuda1 aMaciej Biel00aStatistical and Econometric Analysis  of Selected Effects of COVID-19 Pandemic0 v401322nas a2200133   4500000000100000000000100001008004100002260009600043100001900139245003800158856026000196520071000456020002201166       2025                            d  c01/2025b1st International Conference PUT STEM Day 2025: Book of AbstractsaPoznań, Polska1 aMarzena Halama00aOptimization for Pre-trained CNNs  uhttps://putpoznanpl-my.sharepoint.com/personal/stemday_put_poznan_pl/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fstemday%5Fput%5Fpoznan%5Fpl%2FDocuments%2FKN%20PUT%20STEM%2FStemDay%2FSTEM%20Day%202026%2FPUT%20STEM%20Day%202025%2F1st%20International%20Confe3 aConvolutional neural networks (CNNs) constitute a fundamental cornerstone of computer vision. With increasing complexity, the need for effective optimisation strategies remains crucial. Techniques such as transfer learning (TL), utilising pre-trained networks, enable the deployment of advanced models on mobile devices with limited computing capacity, including autonomous vehicles and educational applications. The study explores optimisation strategies for Keras models, focusing on the impact of different algorithms on performance and accuracy. The results demonstrate that appropriate optimiser selection enhances learning efficiency, mitigates overlearning, and supports accurate image recognition.  a978-83-955437-7-701478nas a2200109   4500000000100000008004100001100001900042700002200061245010500083520116600188022001401354       2026                            d1 aMarzena Halama1 aNatalia Polańska00aBielik i GPT w analizie polskiego prawa karnego: wpływ RAG  i grafów wiedzy na jakość odpowiedzi3 a<p><span style="font-family:&quot;Times New Roman&quot;,serif;font-size:11.0pt;" lang="EN-US">Rozwój dużych modeli językowych stwarza nowe możliwości wspomagania wyszukiwania i analizy informacji prawnych. Problemem pozostaje ryzyko, że modele generują wiarygodnie brzmiące odpowiedzi, które nie mają podstaw w przepisach lub orzecznictwie, zwłaszcza gdy trzeba połączyć informacje z kilku źródeł prawnych. Celem badania było porównanie jakości odpowiedzi modeli językowych na pytania z zakresu polskiego prawa karnego z wykorzystaniem różnych sposobów dostępu do wiedzy zewnętrznej. Analizie poddano 4 warianty: model bez kontekstu; Retrieval-Augmented Generation (RAG) oparty na wyszukiwaniu wektorowym; rozwiązanie wykorzystujące graf wiedzy oraz architekturę hybrydową łączącą wyszukiwanie wektorowe i&nbsp;grafowe. Badanie obejmowało ocenę podobieństwa semantycznego i&nbsp;kompletności odpowiedzi, jakości wyszukiwania źródeł oraz zdolności do rozpoznawania braku informacji w&nbsp;korpusie. Szczególną uwagę poświęcono pytaniom wymagającym łączenia informacji pochodzących z wielu źródeł.</span></p>  a1734-5391