Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations
Accurate path loss prediction is essential for optimizing Long-Range Wide-Area Network (LoRaWAN) performance. Previous studies have employed various Machine Learning (ML) models for path loss prediction. However, environmental factors such as temperature, humidity, barometric pressure, and particula...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4101 |
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| author | Maram A. Alkhayyal Almetwally M. Mostafa |
| author_facet | Maram A. Alkhayyal Almetwally M. Mostafa |
| author_sort | Maram A. Alkhayyal |
| collection | DOAJ |
| description | Accurate path loss prediction is essential for optimizing Long-Range Wide-Area Network (LoRaWAN) performance. Previous studies have employed various Machine Learning (ML) models for path loss prediction. However, environmental factors such as temperature, humidity, barometric pressure, and particulate matter have been largely neglected. This study bridges this gap by evaluating the performance of five boosting ML models—AdaBoost, XGBoost, LightGBM, GentleBoost, and LogitBoost—under dynamic environmental conditions. The models were compared with theoretical models (Log-Distance and Okumura-Hata) and existing studies that employed the same dataset based on metrics such as RMSE, MAE, and R<sup>2</sup>. Furthermore, a detailed performance vs. complexity analysis was conducted using metrics such as training time, inference latency, model size, and energy consumption. Notably, barometric pressure emerged as the most influential environmental factor affecting path loss across all models. Bayesian Optimization was applied to fine-tune hyperparameters to improve model accuracy. Results showed that LightGBM outperformed other models with the lowest RMSE of 0.5166 and the highest R<sup>2</sup> of 0.7151. LightGBM also offered the best trade-off between accuracy and computational efficiency. The findings show that boosting algorithms, particularly LightGBM, are highly effective for path loss prediction in LoRaWANs. |
| format | Article |
| id | doaj-art-d6191102aa384593bb90154917e50abc |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d6191102aa384593bb90154917e50abc2025-08-20T03:29:02ZengMDPI AGSensors1424-82202025-06-012513410110.3390/s25134101Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental VariationsMaram A. Alkhayyal0Almetwally M. Mostafa1Department of Information Systems, College of Computers and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Information Systems, College of Computers and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaAccurate path loss prediction is essential for optimizing Long-Range Wide-Area Network (LoRaWAN) performance. Previous studies have employed various Machine Learning (ML) models for path loss prediction. However, environmental factors such as temperature, humidity, barometric pressure, and particulate matter have been largely neglected. This study bridges this gap by evaluating the performance of five boosting ML models—AdaBoost, XGBoost, LightGBM, GentleBoost, and LogitBoost—under dynamic environmental conditions. The models were compared with theoretical models (Log-Distance and Okumura-Hata) and existing studies that employed the same dataset based on metrics such as RMSE, MAE, and R<sup>2</sup>. Furthermore, a detailed performance vs. complexity analysis was conducted using metrics such as training time, inference latency, model size, and energy consumption. Notably, barometric pressure emerged as the most influential environmental factor affecting path loss across all models. Bayesian Optimization was applied to fine-tune hyperparameters to improve model accuracy. Results showed that LightGBM outperformed other models with the lowest RMSE of 0.5166 and the highest R<sup>2</sup> of 0.7151. LightGBM also offered the best trade-off between accuracy and computational efficiency. The findings show that boosting algorithms, particularly LightGBM, are highly effective for path loss prediction in LoRaWANs.https://www.mdpi.com/1424-8220/25/13/4101boosting algorithmsenvironmental variationsLoRaWANpath loss |
| spellingShingle | Maram A. Alkhayyal Almetwally M. Mostafa Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations Sensors boosting algorithms environmental variations LoRaWAN path loss |
| title | Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations |
| title_full | Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations |
| title_fullStr | Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations |
| title_full_unstemmed | Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations |
| title_short | Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations |
| title_sort | enhancing lorawan performance using boosting machine learning algorithms under environmental variations |
| topic | boosting algorithms environmental variations LoRaWAN path loss |
| url | https://www.mdpi.com/1424-8220/25/13/4101 |
| work_keys_str_mv | AT maramaalkhayyal enhancinglorawanperformanceusingboostingmachinelearningalgorithmsunderenvironmentalvariations AT almetwallymmostafa enhancinglorawanperformanceusingboostingmachinelearningalgorithmsunderenvironmentalvariations |