Deep Reinforcement Learning-Based Joint Sequence Scheduling and Trajectory Planning in Wireless Rechargeable Sensor Networks

Mobile charging has become a popular and efficient method for replenishing energy. This is done with a mobile charger (MC) and wireless energy transfer technology (WET), which helps to alleviate the issue of energy constraints in wireless rechargeable sensor networks (WRSNs). Notably, designing mobile charging scheduling schemes is essential for improving charging performance. Most current studies assume that the networks are obstacle free. Unlike the existing studies, this article focuses on joint sequence scheduling and trajectory planning problem (JSSTP), which assume that the network has multiple static obstacles. To address this issue, we propose a novel deep reinforcement learning-based JSSTP (DRL-JSSTP) that enables the MC to avoid obstacles and reach the charging target to charge the sensors fully. This approach maximizes energy usage efficiency and sensor survival rate while satisfying MC energy capacity constraints. DRL-JSSTP includes a charging target selector (CTS) and a trajectory planner (TP), which determine the index of the next charging target and plan the movement trajectory to avoid obstacles, respectively. We adopt 1-D convolutional neural networks (CNNs) to extract feature information about the environment state and gated recurrent units (GRUs) to predict the charging decisions. Simulation results demonstrate that DRL-JSSTP outperforms existing approaches, achieving higher energy usage efficiency and sensor survival rate.