Year: 2025
Type: article
Abstract: The pervasive growth of the Internet of Things (IoT) necessitates efficient communication technologies, among which Long Range Wide Area Network (LoRaWAN) is prominent due to its long-range, low-power characteristics. A significant challenge in dense LoRaWAN deployments is the efficient management of resources, particularly Spreading Factor (SF) allocation. In this paper, we propose a machine learning-based approach for optimal SF allocation to enhance network performance. We developed a simulation-driven framework utilizing the ns-3 simulator to generate a comprehensive dataset mapping network conditions, including RSSI, SNR, device coordinates, and distance to the gateway, to optimal SF assignments determined through an energy-aware optimization process. An XGBoost model was trained on this dataset to predict the optimal SF based on real-time network parameters. Our methodology focuses on balancing packet delivery ratio and energy consumption. The performance evaluation demonstrates that the trained XGBoost model effectively classifies optimal SFs, exhibiting strong diagonal dominance in the confusion matrix and achieving competitive accuracy with efficient computational characteristics, making it suitable for resource-constrained LoRaWAN environments.
Authors: Farhan Nisar, Muhammad Amin, Muhammad Touseef Irshad, Hassan Jalil Hadi, Naveed Ahmad, Mohamad Ladan
Language: English
Topic: IoT Networks and Protocols
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
Funder: Prince Sultan University
OpenAlex ID: W4415237159