Open Access

ARTICLE

Enhancing LoRaWAN Sensor Networks: A Deep Learning Approach for Performance Optimizing and Energy Efficiency

Maram Alkhayyal^*, Almetwally M. Mostafa

Department of Information Systems, College of Computers and Information Sciences, King Saud University, Riyadh, P.O. Box 51178, Saudi Arabia

* Corresponding Author: Maram Alkhayyal. Email:

Computers, Materials & Continua 2025, 83(1), 1079-1100. https://doi.org/10.32604/cmc.2025.061836

Received 04 December 2024; Accepted 12 February 2025; Issue published 26 March 2025

Abstract

The rapid expansion of the Internet of Things (IoT) has led to the widespread adoption of sensor networks, with Long-Range Wide-Area Networks (LoRaWANs) emerging as a key technology due to their ability to support long-range communication while minimizing power consumption. However, optimizing network performance and energy efficiency in dynamic, large-scale IoT environments remains a significant challenge. Traditional methods, such as the Adaptive Data Rate (ADR) algorithm, often fail to adapt effectively to rapidly changing network conditions and environmental factors. This study introduces a hybrid approach that leverages Deep Learning (DL) techniques, namely Long Short-Term Memory (LSTM) networks, and Machine Learning (ML) techniques, namely Artificial Neural Networks (ANNs), to optimize key network parameters such as Signal-to-Noise Ratio (SNR) and Received Signal Strength Indicator (RSSI). LSTM-ANN model trained on the “LoRaWAN Path Loss Dataset including Environmental Variables” from Medellín, Colombia, and the model demonstrated exceptional predictive accuracy, achieving an R² score of 0.999, Mean Squared Error (MSE) of 0.041, Root Mean Squared Error (RMSE) of 0.203, and Mean Absolute Error (MAE) of 0.167, significantly outperforming traditional regression-based approaches. These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN. By providing a scalable and adaptive solution for large-scale IoT deployments, this work lays the foundation for real-world implementation, emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.

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