How can AI optimize mobile charger routes for long-lasting sensor networks?
Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable Sensor Networks
This research addresses the problem of maximizing the operational lifespan of a Wireless Rechargeable Sensor Network (WRSN) tasked with monitoring specific targets and maintaining communication with a base station. Mobile chargers (MCs) replenish sensor energy, and the challenge is to optimize their movement and charging schedules to prevent sensors from running out of power and losing connection.
The system uses a decentralized, partially observable, semi-Markov decision process (Dec-POSMDP) model, enabling independent decision-making by each MC. A novel asynchronous multi-agent proximal policy optimization (AMAPPO) algorithm, based on proximal policy optimization (PPO) and incorporating a U-Net for spatial action prediction, is employed to train the MCs. This approach allows for efficient coordination among MCs in the asynchronous setting of a WRSN, optimizing their actions without direct communication. The framework also generalizes to different network topologies and sizes without retraining. While focusing on physical sensor networks, the decentralized and asynchronous nature of the proposed Dec-POSMDP and AMAPPO are directly relevant to developing LLM-based multi-agent systems, offering a framework for coordination and scaling. The use of a U-Net for processing spatial information is also potentially applicable for LLMs handling spatially structured data.