Authors
Abdelrahman Radwan1, Nazhatul Kamarudin1, Mahmud Iwan Solihin1, Hungyang
Leong1, Mohamed Rizon1, *, Hazry Desa2, Muhammad Azizi Bin Azizan2
1Faculty of Engineering, Technology and Built Environment, UCSI University,
Jalan Puncak Menara Gading, Taman Connaught, Kuala Lumpur 56000, Malaysia
2Centre of Excellence for Unmanned Aerial Systems (COEUAS), Universiti
Malaysia Perlis, Block E, Pusat Perniagaan Pengkalan Jaya, Jalan Kangar
– AlorSetar, Kangar, Perlis 01000, Malaysia
*Corresponding author. Email: [email protected]
Corresponding Author
Mohamed Rizon
Received 6 November 2019, Accepted 4 May 2020, Available Online 2 June
2020.
DOI
https://doi.org/10.2991/jrnal.k.200528.008
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 ALife Robotics Corp. Ltd.
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/).