Energy-Efficient Resource Allocation Scheme Based on Reinforcement Learning in Distributed LoRa Networks

The rapid growth of Long Range (LoRa) devices has led to network congestion, reducing spectrum and energy efficiency. To address this problem, we propose an energy-efficient reinforcement learning method for distributed LoRa networks, enabling each device to independently select appropriate transmission parameters, i.e., channel, transmission power (TP), and bandwidth (BW) based on acknowledgment (ACK) feedback and energy consumption. Our method employs the Upper Confidence Bound (UCB)1-tuned algorithm and incorporates energy metrics into the reward function, achieving lower power consumption and high transmission success rates. Designed to be lightweight for resource-constrained IoT devices, it was implemented on real LoRa hardware and tested in dense network scenarios. Experimental results show that the proposed method outperforms fixed allocation, adaptive data rate low-complexity (ADR-Lite), and ϵ-greedy methods in both transmission success rate and energy efficiency.