X-means Clustering for Wireless Sensor Networks

DOI

10.2991/jrnal.k.200528.008How to use a DOI?

Keywords

K-means; X-means; clustering; wireless; sensors; networks

Abstract

K-means clustering algorithms of wireless sensor networks are potential solutions that prolong the network lifetime. However, limitations hamper these algorithms, where they depend on a deterministic K-value and random centroids to cluster their networks. But, a bad choice of the K-value and centroid locations leads to unbalanced clusters, thus unbalanced energy consumption. This paper proposes X-means algorithm as a new clustering technique that overcomes K-means limitations; clusters constructed using tentative centroids called parents in an initial phase. After that, parent centroids split into a range of positions called children, and children compete in a recursive process to construct clusters. Results show that X-means outperformed the traditional K-means algorithm and optimized the energy consumption.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Abdelrahman Radwan
AU  - Nazhatul Kamarudin
AU  - Mahmud Iwan Solihin
AU  - Hungyang Leong
AU  - Mohamed Rizon
AU  - Hazry Desa
AU  - Muhammad Azizi Bin Azizan
PY  - 2020
DA  - 2020/06/02
TI  - X-means Clustering for Wireless Sensor Networks
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 111
EP  - 115
VL  - 7
IS  - 2
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.200528.008
DO  - 10.2991/jrnal.k.200528.008
ID  - Radwan2020
ER  -