Multi-antenna mobile charger scheduling optimization scheme for wireless rechargeable sensor networks

Abstract

Multi-antenna mobile chargers (MC) featuring directional multi-beam functionality present a promising solution for energy replenishment in wireless rechargeable sensor networks. However, existing multi-antenna scheduling schemes encounter challenges in jointly optimizing the coupled problem of Antenna Configuration and Path Planning (ACPP) while balancing MC’s coverage efficiency with energy consumption. To address this gap, this paper investigates the complex interdependencies and stringent constraints inherent in ACPP, and proposes a phased hybrid optimization scheme, PHMS-ACPP, integrating multi-objective optimization and deep reinforcement learning to compute approximate solutions. We first employ a modified Gaussian mixture model incorporating physical coverage constraints via the Expectation–Maximization algorithm to partition clusters, thereby reducing problem complexity. Within each cluster, the subproblem of determining optimal antenna count and orientation is solved using the Multi-objective Grey Wolf Optimizer to simultaneously optimize MC’s coverage efficiency and energy consumption. Then, we utilize Double Deep Q-Network to plan MC’s charging path across clusters, which captures long-term temporal dependencies between the evolution of nodes’ energy states and the spatial allocation of charging resources, enhancing both global scheduling efficacy and long-term charging efficiency. Extensive simulations demonstrate that PHMS-ACPP significantly outperforms state-of-the-art baselines in reducing node failure rate and minimizing average charging delay, with reductions of approximately 21.6% and 14.4%, respectively.