XGBoost-driven adaptive spreading factor allocation for energy-efficient LoRaWAN networks

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.