Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable
This addresses energy management and coverage issues in wireless sensor networks, offering a scalable solution for applications like environmental monitoring, though it is incremental in combining existing methods.
The paper tackles maximizing network lifetime in large-scale wireless rechargeable sensor networks by proposing a decentralized multi-agent reinforcement learning framework for mobile chargers, achieving efficient charging through simultaneous multi-point charging and real-time cooperation without extensive retraining.
The thesis proposes a generalized charging framework for multiple mobile chargers to maximize the network lifetime and ensure target coverage and connectivity in large scale WRSNs. Moreover, a multi-point charging model is leveraged to enhance charging efficiency, where the MC can charge multiple sensors simultaneously at each charging location. The thesis proposes an effective Decentralized Partially Observable Semi-Markov Decision Process (Dec POSMDP) model that promotes Mobile Chargers (MCs) cooperation and detects optimal charging locations based on realtime network information. Furthermore, the proposal allows reinforcement algorithms to be applied to different networks without requiring extensive retraining. To solve the Dec POSMDP model, the thesis proposes an Asynchronous Multi Agent Reinforcement Learning algorithm (AMAPPO) based on the Proximal Policy Optimization algorithm (PPO).