Age of Information Optimization in Laser-Charged UAV-Assisted IoT Networks: A Multi-Agent Deep Reinforcement Learning Method

作者：Geng Sun, Likun Zhang, Jiahui Li, Jing Wu, Jiacheng Wang, Zemin Sun, Changyuan Zhao, Victor C. M. Leung · 发表于：IEEE Transactions on Network Science and Engineering · 年份：2025 · DOI：10.1109/tnse.2025.3596172 · 被引用次数：7 · 研究领域：Age of Information Optimization、IoT and Edge/Fog Computing、CCD and CMOS Imaging Sensors

The integration of unmanned aerial vehicles (UAVs) with Internet of Things (IoT) networks offers promising solutions for efficient data collection. However, the limited energy capacity of UAVs remains a significant challenge. In this case, laser beam directors (LBDs) have emerged as an effective technology for wireless charging of UAVs during operation, thereby enabling sustained data collection without frequent returns to charging stations (CSs). In this work, we investigate the age of information (AoI) optimization in LBD-powered UAV-assisted IoT networks, where multiple UAVs collect data from distributed IoTs while being recharged by laser beams. We formulate a joint optimization problem that aims to minimize the peak AoI while determining optimal UAV trajectories and laser charging strategies. This problem is particularly challenging due to its non-convex nature, complex temporal dependencies, and the need to balance data collection efficiency with energy consumption constraints. To address these challenges, we propose a novel multi-agent proximal policy optimization with temporal memory and multi-agent coordination (MAPPO-TM) framework. Specifically, MAPPO-TM incorporates temporal memory mechanisms to capture the dynamic nature of UAV operations and facilitates effective coordination among multiple UAVs through decentralized learning while considering global system objectives. Simulation results demonstrate that the proposed MAPPO-TM algorithm outperforms conventional ap...