X-means Clustering for Wireless Sensor Networks
- 10.2991/jrnal.k.200528.008How to use a DOI?
- K-means; X-means; clustering; wireless; sensors; networks
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.
- © 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/).
Cite this article
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 -