A comparative study of Gaussian mixture algorithm and K-means algorithm for efficient energy clustering in MWSN

Iman Ameer Ahmad, Muna Mohammed Jawad Al-Nayar, Ali M. Mahmood


Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving energy of up to 92% at 4,500 rounds.


Clustering algorithm; Energy efficient; Gaussian mixture algorithm; K-means algorithm; Mobile wireless sensor networks

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DOI: https://doi.org/10.11591/eei.v12i6.5707


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Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
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