Joint Sensor Selection and Energy Allocation for Tasks-driven Mobile Charging in Wireless Rechargeable Sensor Networks

Wireless power transfer (WPT) has emerged as a promising paradigm to charge devices due to the high reliability and efficiency of continuous power supply. Recent studies usually focus on relatively general charging patterns and metrics but neglect the collaborated task execution of nodes that incur charging inefficiency. In this article, we respect the energy requirement diversity among nodes to investigate the collaborated and tasks-driven mobile charging problem. Our goal is to maximize the overall task utility that concerns sensor selection and task cooperation. To address this problem, we propose a $(1-1/e)/4$ -approximation algorithm. First, we propose a novel energy allocation scheme with a specific theoretical analysis of the submodularity and gap property for the surrogate function. Then, we approximate the traveling cost to transform the formulated problem into an essentially monotone submodular function optimization subject to a general routing constraint and propose a greedy algorithm to address this problem. We conduct extensive simulations to validate our theoretical results and the results show our algorithm can achieve a near-optimal solution covering at least 84.9% of the optimal result achieved by the OPT algorithm. Furthermore, field experiments in an office room and a soccer field environment are implemented, respectively, to validate our proposed algorithm.