Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method

2025-07-11

AI Summary

This work addresses the Age of Information (AoI) minimization problem in laser-powered UAV-assisted IoT networks, subject to energy constraints. We jointly optimize UAV trajectory planning and laser wireless charging scheduling to minimize peak AoI. To this end, we propose a multi-agent proximal policy optimization (MA-PPO) framework augmented with a temporal memory mechanism, enabling decentralized cooperative decision-making and adaptive response to dynamic environments. Our key innovations include the integration of temporal modeling, distributed control, and physics-informed laser energy transfer modeling into the reinforcement learning architecture—effectively tackling non-convexity and resource coupling challenges. Simulation results demonstrate that the proposed method reduces peak AoI by 15.1% compared to baseline multi-agent deep reinforcement learning approaches, while significantly improving both energy efficiency and data freshness.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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 approaches in terms of peak AoI minimization and energy efficiency. Ideally, the proposed algorithm achieves up to 15.1% reduction in peak AoI compared to conventional multi-agent deep reinforcement learning (MADRL) methods.

Problem

• Optimize age of information in UAV-assisted IoT networks
• Balance data collection efficiency with energy constraints
• Develop multi-agent deep reinforcement learning for UAV coordination

Innovation

• Laser-charged UAVs for sustained IoT data collection
• Multi-agent deep reinforcement learning for optimization
• Temporal memory enhances UAV coordination and efficiency