Enhancing LoRa-based Outdoor Localization Accuracy Using Machine Learning
| Autorzy | Kelesoglu N.; Halama M.; Strzoda A. |
|---|---|
| Tytuł | Enhancing LoRa-based Outdoor Localization Accuracy Using Machine Learning |
| Czasopismo | IEEE Access |
| Rok | 2025 |
| Status | Published |
| Tom | 13 |
| DOI | 10.1109/ACCESS.2025.3589032 |
| URL | https://ieeexplore.ieee.org/document/11080010 |
| Abstrakt | <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> |
| ISSN | 2169-3536 |