A Density-Based Clustering Approach for Optimal Energy Replenishment in WRSNs

Abstract

This paper investigates a density-based clustering approach to obtain optimal stop locations of the mobile charger (MC) and achieve efficient energy replenishment in wireless rechargeable sensor networks (WRSNs). Sensor nodes with charging request are divided into several clusters. Some of these nodes are selected as head nodes, adopting a mobile charger to visit, charging all nodes in the same cluster simultaneously. Different from other clustering algorithms, our proposed clustering approach selects the head nodes with high local density, and the distance between high-density nodes is taken into consideration, effectively avoiding clusters from overlapping and eventually decreasing the charging delay. The local density is related to both the energy condition and the number of neighbor nodes. Simulation results show that our proposed clustering approach can obtain much lower cost caused by the mobile charger's traveling and realize higher charging efficiency than the common method without clustering. Moreover, compared with the k-means based location selection design, the energy supply performance in term of the charging delay can be reduced and the results are more stable due to our proposed clustering approach.