A novel k-hop Compound Metric Based Clustering scheme for ad hoc wireless networks

• Published in: IEEE transactions on wireless communications Vol. 8; no. 1; pp. 367 - 375
• Main Authors: SUPENG LENG, YAN ZHANG, CHEN, Hsiao-Hwa, LIREN ZHANG, KE LIU
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ad hoc networks; Ad hoc wireless network; Applied sciences; Clustering; Clustering algorithms; Clusters; Construction; Exact sciences and technology; Information, signal and communications theory; Laboratories; Large-scale systems; Links; Load balancing (computing); mobility; Network topology; Networks; Routing; Scalability; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Simulation; Switches; Telecommunications and information theory; Wireless communication; Wireless networks; Wireless sensor networks; Automatic classification; mobility; Scalability; Simulation; Wireless telecommunication; Ad hoc wireless network; Connectedness; Metric; clustering; Load balancing; Ad hoc network; Signal classification
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/T-WC.2009.080186

This paper presents a novel k-hop compound metric based clustering (KCMBC) scheme, which uses the host connectivity and host mobility jointly to select cluster-heads. KCMBC is a fast convergent and load balancing clustering approach that is able to offer significant improvement on scalability for large-scale ad hoc networks. On the other hand, since host mobility has been taken into account in terms of the average link expiration time, the clusters constructed by KCMBC are more stable than many other schemes. Simulation results show that the clusters created by using the KCMBC approach retain modest but more uniform cluster size, and cluster-head life-time can be increased by KCMBC up to 50%. Moreover, the control overheads for cluster formation using the KCMBC scheme are kept relatively low if compared to other clustering schemes.