Energy replenishment optimisation via density-based clustering
by Xin Gu; Jun Peng; Yijun Cheng; Xiaoyong Zhang; Kaiyang Liu
International Journal of Computational Science and Engineering (IJCSE), Vol. 21, No. 2, 2020

Abstract: This paper investigates a density-based clustering approach to achieve efficient energy replenishment in wireless rechargeable sensor networks (WRSNs). Sensor nodes with charging request are divided into several clusters. Some of them are selected as head nodes, adopting a mobile charger to visit. The rest are arranged to the closest head nodes. Then the mobile charger serves all nodes in the same cluster simultaneously. Different from other clustering algorithms, our proposed clustering approach selects the head nodes with high local density. The distance between high-density nodes is also taken into consideration, effectively reducing the charging delay. Simulation results show that our proposed clustering approach can achieve optimal cluster results. Moreover, compared with two other cluster-based charging methods, the charging delay and travel distance can be reduced due to our proposed clustering approach, in both dense and sparse deployment scenarios.

Online publication date: Wed, 11-Mar-2020

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