Energy-Aware Multiple Mobile Chargers Coordination for Wireless Rechargeable Sensor Networks

作     者：Mo, Lei Kritikakou, Angeliki He, Shibo 

作者机构：Univ Rennes CNRS INRIA IRISA F-35042 Rennes France Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2019年第6卷第5期

页      面：8202-8214页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61403340, 61672458] ANR Approximative Flexible Circuits and Computing for IoT Project [ANR-15-CE25-0015] Zhejiang Natural Science Foundation [LR16F020001] Agence Nationale de la Recherche (ANR) [ANR-15-CE25-0015] Funding Source: Agence Nationale de la Recherche (ANR) 

主　　题：Decomposition method mixed-integer linear program mobile charger (MC) coordination perpetual operation wireless rechargeable sensor networks (WRSNs) 

摘      要：Wireless charging provides dynamic power supply for wireless sensor networks (WSNs). Such systems, are typically considered under the scenario of wireless rechargeable sensor networks (WRSNs). With the use of mobile chargers (MCs), the flexibility of WRSNs is further enhanced. However, the use of MCs poses several challenges during the system design. The coordination process has to simultaneously optimize the scheduling, the moving time, and the charging time of multiple MCs under limited system resources (time and energy). Efficient methods that jointly solve these challenges are generally lacking in the literature. In this paper, we address the multiple MCs coordination problem under multiple system requirements. First, we aim at minimizing the energy consumption of MCs, guaranteeing that every sensor will not run out of energy. We formulate the multiple MCs coordination problem as a mixed-integer linear programming and derive a set of desired network properties. Second, we propose a novel decomposition method to optimally solve the problem, as well as to reduce the computation time. Our approach divides the problem into a subproblem for the MC scheduling and a subproblem for the MC moving time and charging time, and solves them iteratively by utilizing the solution of one into the other. The convergence of proposed method is analyzed theoretically. Simulation results demonstrate the effectiveness and scalability of the proposed method in terms of solution quality and computation time.