A Multi-parameter Weighted Clustering Algorithm for Mobile Ad Hoc Networks ⋆

Clustering is an efficient way of network topology management, and the hierarchical structure obtained by clustering algorithm can largely improve the performance of Mobile Ad Hoc Network (MANET). To better accommodate MANET, we propose a Multi-parameter Weighted Clustering (MWC) algorithm which takes into consideration three parameters: residual power, connectivity, and average mobility. We also designed different average mobility parameter for two typical MANET models, that is, Relative Stability (RS) for Random Walk Mobility (RWM) networks and Moving Correlation (MC) for Reference Point Group Mobility (RPGM) networks. Simulation results showed that the proposed algorithm has a better performance than HD and MOBIC in three aspects: topology simplicity, environment adaptability, and cluster stability.

[1]  Chor Ping Low,et al.  Efficient Load-Balanced Clustering Algorithms for wireless sensor networks , 2008, Comput. Commun..

[2]  Zhiping Zhou,et al.  An energy balanced cluster algorithm for wireless sensor networks , 2012, 2012 24th Chinese Control and Decision Conference (CCDC).

[3]  Sajal K. Das,et al.  WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks , 2002, Cluster Computing.

[4]  Pascal Lorenz,et al.  Connectivity, Energy and Mobility Driven Clustering Algorithm for Mobile Ad Hoc Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[5]  Igor Leão dos Santos,et al.  WSNs clustering based on semantic neighborhood relationships , 2012, Comput. Networks.

[6]  Xiang Min,et al.  Energy efficient clustering algorithm for maximizing lifetime of wireless sensor networks , 2010 .

[7]  Jiguo Yu,et al.  Constructing minimum extended weakly-connected dominating sets for clustering in ad hoc networks , 2012, J. Parallel Distributed Comput..

[8]  Ali Khosrozadeh,et al.  Clustering Algorithms in Mobile Ad Hoc Networks , 2009, 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology.

[9]  G. Radhamani,et al.  Clustering schemes for mobile adhoc networks: A review , 2012, 2012 International Conference on Computer Communication and Informatics.

[10]  Weijia Jia,et al.  WSN19-4: Efficient Construction of Weakly-Connected Dominating Set for Clustering Wireless Ad Hoc Networks , 2006, IEEE Globecom 2006.

[11]  Hui Chen,et al.  Improved AOW clustering algorithms for wireless self-organized network and performance analysis , 2012, CSQRWC 2012.

[12]  Prithwish Basu,et al.  A mobility based metric for clustering in mobile ad hoc networks , 2001, Proceedings 21st International Conference on Distributed Computing Systems Workshops.

[13]  Samir Al-Khayatt,et al.  An efficient weighted distributed clustering algorithm for mobile ad hoc networks , 2010, The 2010 International Conference on Computer Engineering & Systems.

[14]  Jiehui Chen,et al.  A Distributed Clustering Algorithm for Voronoi Cell-Based Large Scale Wireless Sensor Network , 2010, 2010 International Conference on Communications and Mobile Computing.

[15]  Shervin Erfani,et al.  Survey of multipath routing protocols for mobile ad hoc networks , 2009, J. Netw. Comput. Appl..

[16]  C. Vangelatos,et al.  Empirical Study of Clustering Algorithms for Wireless Ad Hoc Networks , 2009, 2009 16th International Conference on Systems, Signals and Image Processing.

[17]  Zhezhuang Xu,et al.  Hybrid Clustering and Routing Strategy with Low Overhead for Wireless Sensor Networks , 2010, 2010 IEEE International Conference on Communications.

[18]  Yan Zhang,et al.  A distributed group mobility adaptive clustering algorithm for mobile ad hoc networks , 2009, Comput. Commun..