Machine learning-based joint optimization for LoRaWAN reliability and energy efficiency

Abstract

Dense long-range wide area network (LoRaWAN) deployments often experience reduced packet delivery reliability (PDR) and increased energy consumption due to suboptimal adaptive data rate (ADR) mechanisms. This study proposes a machine learning (ML) based joint optimization method to improve PDR or reliability while minimizing node energy consumption. The proposed solution employs random forest (RF) and light gradient boosting machine (LightGBM) models, which are suitable for resource constrained end devices (ED). Using combinations of data rate (DR), frequency channel, and transmission power (TxPower) from 500 ED, the model extracts 14 features for parameter optimization. Unlike conventional ADR, the proposed approach can learn complex relationships beyond the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) parameters. The model achieves higher prediction accuracy within a 5 km coverage area. Network simulator 3 (NS-3) simulation results demonstrate better performance and energy efficiency compared to conventional ADR. The proposed approach improves PDR by 6.1–11.6% and reduces node energy consumption by 20–25%, providing a better balance between communication reliability and energy efficiency in scalable LoRaWAN networks.