The rapid advancement of Low-Power Wide Area Networks (LPWANs), particularly Long Range (LoRa) systems, has positioned them as a cornerstone for Next-Generation Internet of Things (NG-IoT) applications within 5G/6G ecosystems. Despite their long-range and low-power advantages, achieving high energy efficiency in LoRa networks remains a significant challenge in highly dynamic environments. Traditional terrestrial gateway deployments often suffer from coverage gaps and non-line-of-sight propagation, while satellite-based alternatives incur excessive energy consumption and prohibitive latency. To address these limitations, we propose a multi-UAV architecture where unmanned aerial vehicles (UAVs) serve as mobile LoRa gateways to dynamically collect data from ground-based end devices (EDs). We formulate a joint optimization problem to maximize the system's weighted energy efficiency by jointly optimizing spreading factors, transmission powers, UAV trajectories, and ED-UAV associations. This problem is transformed into a partially observable stochastic game (POSG), which we solve using our proposed Green LoRa Multi-Agent Proximal Policy Optimization (GLo-MAPPO). Our framework leverages centralized training with decentralized execution (CTDE) and is enhanced by a gain-based ED-UAV association scheme. Simulation results show that GLo-MAPPO significantly outperforms state-of-the-art multi-agent reinforcement learning (MARL) benchmarks in energy efficiency and power consumption across varying network densities. Furthermore, ablation studies validate the necessity of each optimization component and the effectiveness of the proposed association scheme.

翻译：低功耗广域网（LPWAN）的快速发展，特别是长距离（LoRa）系统，已将其定位为5G/6G生态系统中下一代物联网（NG-IoT）应用的基石。尽管LoRa网络具有长距离和低功耗的优势，但在高度动态环境中实现高能效仍是一个重大挑战。传统的地面网关部署常存在覆盖盲区和非视距传播问题，而基于卫星的替代方案则会产生过高的能耗和难以接受的延迟。为解决这些限制，我们提出了一种多无人机架构，其中无人机（UAV）作为移动LoRa网关，动态收集来自地面终端设备（ED）的数据。我们建立了一个联合优化问题，通过联合优化扩频因子、发射功率、无人机轨迹和ED-UAV关联，最大化系统的加权能效。该问题被转化为部分可观测随机博弈（POSG），并利用我们提出的绿色LoRa多智能体近端策略优化（GLo-MAPPO）算法进行求解。我们的框架采用集中式训练与分散式执行（CTDE），并通过基于增益的ED-UAV关联方案加以增强。仿真结果表明，在不同网络密度下，GLo-MAPPO在能效和功耗方面显著优于最先进的多智能体强化学习（MARL）基准方法。此外，消融研究验证了每个优化组件的必要性以及所提关联方案的有效性。