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

2025-09-22

AI Summary

To address the low energy efficiency, uneven terrestrial coverage, high energy consumption, and large latency of LoRa-based LPWANs in large-scale or dynamic scenarios, this paper proposes a novel multi-UAV cooperative aerial gateway architecture. It jointly optimizes spreading factor, transmission power, UAV trajectories, and device association. Innovatively, this work is the first to introduce multi-agent reinforcement learning (MARL) for LoRa energy-efficiency optimization, designing the GLo-MAPPO framework based on centralized training with decentralized execution (CTDE) and formulating the problem as a partially observable Markov decision process (POMDP). Simulation results demonstrate that, under scenarios with 10–50 end devices, the proposed method improves energy efficiency by 49.95%–71.25% over baseline approaches, significantly outperforming conventional solutions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Achieving high energy efficiency in large-scale LoRa networks for IoT applications
• Overcoming coverage gaps and NLoS losses in traditional terrestrial LoRa deployments
• Optimizing spreading factors, transmission powers, and UAV trajectories simultaneously

Innovation

• Uses multiple UAVs as flying LoRa gateways
• Optimizes spreading factors, powers, and UAV trajectories
• Employs multi-agent reinforcement learning with CTDE