An On-Demand Partial Charging Algorithm without Explicit Charging Request for WRSNs

Abstract

Wireless rechargeable sensor networks provide an effective solution to the energy limitation problem in wireless sensor networks by introducing chargers to recharge the nodes. On-demand charging algorithms, which schedule the mobile charger to charge the most energy-scarce node based on the node’s energy status, are one of the main types of charging scheduling algorithms for wireless rechargeable sensor networks. However, most existing on-demand charging algorithms require a predefined charging request threshold to prompt energy-starved nodes with energy levels lower than this threshold to submit an explicit charging request to the base station so that the base station can schedule the mobile charger to charge these nodes. These algorithms ignore the difference in importance of nodes in the network, and charging requests sent by nodes independently can interfere with the mobile charger’s globally optimal scheduling. In addition, forwarding charging requests in the network increases the network burden. In this work, aiming to maximize the network revenue and the charging efficiency, we investigate the problem of scheduling the mobile charger on-demand without depending on explicit charging requests from nodes (SWECR). We propose a novel on-demand partial charging algorithm that does not require explicit charging requests from nodes. Our algorithm accounts for the differences in importance between nodes and leverages the deep reinforcement learning technique to determine the target charging node and each node’s charging time. The simulation results demonstrate that the proposed algorithm significantly improves the charging performance and maximizes the network revenue and the charging efficiency.