Research Repository

Data Mining via Minimal Spanning Tree Clustering For Prolonging Lifetime of Wireless Sensor Networks

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

Full text for this resource is not available from the Research Repository.


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
Uncontrolled Keywords: ResPubID18730, clustering, data mining, energy efficiency, minimal spanning tree, wireless sensor networks
Subjects: Current > FOR Classification > 0802 Computation Theory and Mathematics
Current > FOR Classification > 0806 Information Systems
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Related URLs:
Depositing User: VUIR
Date Deposited: 07 May 2012 05:35
Last Modified: 07 May 2012 05:35
ePrint Statistics: View download statistics for this item
Citations in Scopus: 6 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar