Q-learning-based distributed multi-charging algorithm for large-scale WRSNs

抄録

Wireless Rechargeable Sensors Network (WRSN) has recently emerged as a promising solution to solve the energy limitation of WRSN. This study considers large-scale WRSNs, where many Mobile Chargers (MCs) are placed to ensure the target coverage and connectivity. We propose a distributed charging algorithm that allows MCs to decide their optimal charging path and charging time. Our proposal is based on the Q-learning technique, where every MC maintains a Q-table that measures the goodness of actions. According to the evaluation, the proposed charging scheme extended the network lifetime by 3.52 times in average and 5.06 times in the best case.