A Novel On-Demand Charging Strategy Based on Swarm Reinforcement Learning in WRSNs

The charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless energy transfer technology, electrical energy can be transfer from Wireless Charging Equipment (WCE) to the sensor nodes, providing a new paradigm to prolong the network lifetime. Existing research usually takes the periodical and deterministic charging approach, but ignore the limited energy of the WCE and the influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for real networks. In this study, we aim to minimize the number of dead sensor nodes while maximizing energy utilization of WCE under the limited energy of the WCE. Furthermore, the Swarm Reinforcement Learning (SRL) method is firstly introduced to achieve the autonomous planning ability of WCE. Moreover, to solve the problem of insufficient search in existing SRL algorithm, we improve the SRL by firefly algorithm. And a novel charging algorithm, named Swarm Reinforcement Learning based on Firefly Algorithm (SRL-FA), is proposed for the on-demand charging architecture. To evaluate the performance of the proposed algorithm, SRL-FA is compared with the existing swarm reinforcement learning algorithms and classic on-demand charging algorithms in two network scenarios. The Extensive simulation shows that the proposed algorithm can achieve promising performance in energy utilization of WCE, charging success rate and other performance metrics.