Data Mining via Minimal Spanning Tree Clustering For Prolonging Lifetime of Wireless Sensor Networks
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Huang, Guangyan, Li, Xiaowei, He, Jing and Li, Xin (2007) Data Mining via Minimal Spanning Tree Clustering For Prolonging Lifetime of Wireless Sensor Networks. International Journal of Information Technology and Decision Making, 6 (2). pp. 235-251. ISSN 0219-6220
Abstract
Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively.
| Item type | Article |
| URI | https://vuir.vu.edu.au/id/eprint/3308 |
| Subjects | Historical > FOR Classification > 0802 Computation Theory and Mathematics Historical > FOR Classification > 0806 Information Systems Historical > Faculty/School/Research Centre/Department > School of Engineering and Science |
| Keywords | ResPubID18730, clustering, data mining, energy efficiency, minimal spanning tree, wireless sensor networks |
| Citations in Scopus | 6 - View on Scopus |
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