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Sensors 2017, 17(10), 2290;

Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks

Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, Shenyang 110819, China
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Department of Informatics, King’s College London, London WC2R 2LS, UK
Author to whom correspondence should be addressed.
Received: 9 August 2017 / Revised: 27 September 2017 / Accepted: 30 September 2017 / Published: 9 October 2017
(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks)
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The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms. View Full-Text
Keywords: WRSNs; charging efficiency; routing; GA; heuristic algorithm WRSNs; charging efficiency; routing; GA; heuristic algorithm

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Jia, J.; Chen, J.; Deng, Y.; Wang, X.; Aghvami, A.-H. Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks. Sensors 2017, 17, 2290.

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