| Autor: |
Nazhatul Hafizah Kamarudin, Abdelrahman Radwan, Mohamed Rizon, Muhammad Azizi Azizan, Desa Hazry, Mahmud Iwan Solihin, Hungyang Leong |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Journal of Robotics, Networking and Artificial Life (JRNAL), Vol 7, Iss 2 (2020) |
| ISSN: |
2352-6386 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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