Clustering and splitting charging algorithms for large scaled wireless rechargeable sensor networks

Abstract

As the interdiscipline of wireless communication and control engineering, the periodical charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. However, existing techniques for periodical charging neglect to focus on the location relationship and topological feature, leading to large charging times and long traveling time. In this paper, we develop a hybrid clustering charging algorithm (HCCA), which firstly constructs a network backbone based on a minimum connected dominating set built from the given network. Next, a hierarchical clustering algorithm which takes advantage of location relationship, is proposed to group nodes into clusters. Afterward, a K-means clustering algorithm is implemented to calculate the energy core set for realizing energy awareness. To further optimize the performance of HCCA, HCCA-TS is proposed to transform the energy charging process into a task splitting model. Tasks generated from HCCA are split into small tasks, which aim at reducing the charging time to enhance the charging efficiency. At last, simulations are carried out to demonstrate the merit of the schemes. Simulation results indicate that HCCA can enhance the performance in terms of reducing charging times, journey time and average charging time simultaneously. Moreover, HCCA-TS can further improve the performance of HCCA.