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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 13; p. 4101
Main Authors: Alkhayyal, Maram A., Mostafa, Almetwally M.
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 30.06.2025
MDPI
Subjects:
Accuracy
Algorithms
Atmospheric pressure
boosting algorithms
Communication
Comparative analysis
Data mining
Datasets
Energy consumption
environmental variations
Humidity
Internet of Things
LoRaWAN
Machine learning
Optimization techniques
path loss
environmental variations
boosting algorithms
path loss
LoRaWAN
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary: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 R2. 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 R2 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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25134101