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

TL;DR

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.

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 paper, 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'' (CFSFDP) 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 multi-cluster 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.

Figures (11)

• Figure 1: Example of soft $k$-means clustering.
• Figure 2: An example of KDE. (a) Nodes distribution. (b) 3-dimensional density contour of nodes in (a). (c) 2-dimensional density contour of nodes in (a).
• Figure 3: CFSFDP in two dimensions. (a) Nodes distribution. (b) Decision graph for nodes in (a): X-coordinate is local density $\rho$, and Y-coordinate is $\delta$. (c) Two center nodes are determined.
• Figure 4: Flowchart of the proposed algorithm.
• Figure 5: A node at the boundary of two clusters.