GLo-MAPPO: A Multi-Agent Proximal Policy Optimization for Energy Efficiency in UAV-Assisted LoRa Networks (2509.17676v1)

Abstract: Long Range (LoRa) based low-power wide area networks (LPWANs) are crucial for enabling next-generation IoT (NG-IoT) applications in 5G/6G ecosystems due to their long-range, low-power, and low-cost characteristics. However, achieving high energy efficiency in such networks remains a critical challenge, particularly in large-scale or dynamically changing environments. Traditional terrestrial LoRa deployments often suffer from coverage gaps and non-line-of-sight (NLoS) propagation losses, while satellite-based IoT solutions consume excessive energy and introduce high latency, limiting their suitability for energy-constrained and delay-sensitive applications. To address these limitations, we propose a novel architecture using multiple unmanned aerial vehicles (UAVs) as flying LoRa gateways to dynamically collect data from ground-based LoRa end devices. Our approach aims to maximize the system's weighted global energy efficiency by jointly optimizing spreading factors, transmission powers, UAV trajectories, and end-device associations. Additionally, we formulate this complex optimization problem as a partially observable Markov decision process (POMDP) and propose green LoRa multi-agent proximal policy optimization (GLo-MAPPO), a multi-agent reinforcement learning (MARL) framework based on centralized training with decentralized execution (CTDE). Simulation results show that GLo-MAPPO significantly outperforms benchmark algorithms, achieving energy efficiency improvements of 71.25%, 18.56%, 67.00%, 59.73%, and 49.95% for networks with 10, 20, 30, 40, and 50 LoRa end devices, respectively.