Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
arxiv(2024)
摘要
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.
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