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http://dx.doi.org/10.4218/etrij.2018-0126

Demand-based charging strategy for wireless rechargeable sensor networks  

Dong, Ying (College of Communication Engineering, Jilin University)
Wang, Yuhou (College of Communication Engineering, Jilin University)
Li, Shiyuan (College of Communication Engineering, Jilin University)
Cui, Mengyao (College of Communication Engineering, Jilin University)
Wu, Hao (College of Communication Engineering, Jilin University)
Publication Information
ETRI Journal / v.41, no.3, 2019 , pp. 326-336 More about this Journal
Abstract
A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a wide-spread research problem. In this paper, we propose a demand-based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to-be-charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K-means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on-demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.
Keywords
charging; charging programming; energy balance; wireless rechargeable sensor network;
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Times Cited By KSCI : 2  (Citation Analysis)
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