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.200528.008How 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 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/).