Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks

Abstract

Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this article, we propose an improved soft- $k$ -means (IS- $k$ -means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of clustering by fast search and find of density peaks (CFSFDPs) and kernel density estimation (KDE) to improve the selection of the initial cluster centers of the soft $k$ -means clustering algorithm. Then, we utilize the flexibility of the soft- $k$ -means and reassign member nodes considering their membership probabilities at the boundary of clusters to balance the number of nodes per cluster. Furthermore, the concept of multicluster heads is employed to balance the energy consumption within clusters. Extensive simulation results under different network scenarios demonstrate that for small-scale WSNs with single-hop transmission, the proposed algorithm can postpone the first node death, the half of nodes death, and the last node death on average when compared to various clustering algorithms from the literature.