Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
<|reference_start|>Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks: 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.<|reference_end|>
@article{zhu2024improved,
    title={Improved Soft-k-Means Clustering Algorithm for Balancing Energy
  Consumption in Wireless Sensor Networks},
    author={Botao Zhu, Ebrahim Bedeer, Ha H. Nguyen, Robert Barton, Jerome Henry},
    journal={Published in IEEE Internet of Things Journal, 2021},
    year={2024},
    doi={10.1109/JIOT.2020.3031272},
    archivePrefix={arXiv},
    eprint={2403.15700},
    primaryClass={eess.SY cs.SY}
}