Title: Reinforcement learning approach for quality of coverage-driven mobile charger optimal scheduling in wireless rechargeable sensor networks

Authors: Haoran Wang; Jinglin Li; Tianhang Chen; Wendong Xiao

Addresses: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China ' CNNC Hexin Information Technology (Beijing) Co., Ltd., Beijing 100089, China ' Shunde Innovation School, University of Science and Technology Beijing, Shunde 528399, Guangdong, China ' University of Science and Technology Beijing, Beijing 100083, China; Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China

Abstract: In wireless rechargeable sensor networks (WRSNs), mobile charger (MC) scheduling is one critical issue for improving network utility and resource utilisation efficiency. Traditional charging scheduling approaches usually focus on maximising charging utility while neglecting the importance of network service performance, especially quality of coverage (QoC). In practice, QoC directly affects the integrity of network information acquisition and its effectiveness and reliability. Therefore, the QoC-driven MC optimal scheduling (QCOS) problem is studied, and then a novel reinforcement learning-based mobile charger scheduling algorithm (RL-MCS) is proposed to maximise the network QoC and achieve stable network service performance. Meanwhile, a broadcast charging mechanism is also introduced to improve the overall charging efficiency and reduce the node charging time. In RL-MCS, the real-time energy demand of nodes and the network monitoring performance are considered, which aims to achieve the equilibrium between node survivability and network QoC. In addition, an experience extraction mechanism is designed, which enables MC to make smarter and more prospective charging decisions based on the current network state. Extensive simulations show that RL-MCS significantly outperforms other approaches in improving network QoC and ensuring node survival rate.

Keywords: wireless rechargeable sensor networks; mobile charger scheduling; reinforcement learning; quality of coverage; broadcast charging.

DOI: 10.1504/IJSNET.2025.148200

International Journal of Sensor Networks, 2025 Vol.48 No.4, pp.199 - 211

Received: 19 Mar 2025
Accepted: 13 Apr 2025
Published online: 29 Aug 2025 *

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