A clustering approach based on convergence degree chain for wireless sensor networks. Issue 10 (3rd October 2014)

• Record Type: Journal Article
• Authors: Kuang, Xiao‐Hui; Liu, Li; Liu, Qiang; Li, Xiang; Su, Jinshu; Wang, Xiaofeng; Shi, Weisong; Ray, Indrakshi
• Is Part Of: Security and communication networks. Volume 8:Issue 10(2015)
• Page Start: 1878
• Page End: 1889
• Publication Date: 2014-10-03
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1002/sec.1042
• Languages: English
• ISSNs: 1939-0114

Abstract

<abstract abstract-type="main" id="sec1042-abs-0001"> <title>Abstract</title> <p id="sec1042-para-0001">Wireless sensor networks (WSNs) play an important role in pervasive and ubiquitous systems. The energy consumption, scalability, stability, and lifetime are still open issues in WSNs. Clustering approach has been considered one of the most effective measures and has received tremendous attention in this research area. But most clustering algorithms of the previous related works adopt the idea of periodically and globally regrouping cluster nodes that may cause the improper energy consumption and link state instability during the clustering procedure. Aiming to decrease energy consumption of sensor node and increase WSNs stability, a novel Energy‐efficient Clustering Approach based on Convergence Degree chain, which is termed as ECACD, is proposed in this paper. ECACD protocol can improve stability of topology by using convergence degree and residual energy for cluster head election, reduce energy consumption of member node by cluster joining policy for cluster formation, and decrease communication cost by rotating cluster head according to convergence degree chain generated at initial stage. Analysis and simulation results show the advantage and effectiveness of our approach in terms of the cluster header characteristics and the network life time. According to the comparison with HEED (Hybrid Energy‐Efficient Distributed clustering), which is an energy‐efficient approach for clustering nodes in sensor networks by periodically selecting cluster heads according to a hybrid of their residual energy and a secondary parameter, ECACD increases the stability and extends the network life by 26% and 85%. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>