K-Means clustering of optimized wireless network sensor using genetic algorithm

Azhar M. Kadim, Farah Saad Al-Mukhtar, Najwan Abed Hasan, Aseel B. Alnajjar, Mohammed Sahib Mahdi Altaei


Wireless sensor network is one of the main technology trends that used in several different applications for collecting, processing, and distributing a vast range of data. It becomes an essential core technology for many applications related to sense surrounding environment. In this paper, a two-dimensional WSN scheme was utilized for obtaining various WSN models that intended to be optimized by genetic algorithm for achieving optimized WSN models. Such optimized WSN models might contain two cluster heads that are close to each other, in which the distance between them included in the sensing range, and this demonstrates the presence of a redundant number of cluster heads. This problem exceeded by reapplying the clustering of all sensors found in the WSN model. The distance measure was used to detect handled problem, while K-means clustering was used to redistributing sensors around the alternative cluster head. The result was extremely encouraging in rearranging the dispersion of sensors in the detecting region with a conservative method of modest number of cluster heads that acknowledge the association for all sensors nearby.

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DOI: http://dx.doi.org/10.21533/pen.v10i3.3059


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Copyright (c) 2022 Azhar M. Kadim, Farah Saad Al-Mukhtar, Najwan Abed Hasan, Aseel B. Alnajjar, Mohammed Sahib Mahdi Altaei

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ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License