Using imperialist competitive algorithms in clustering of wireless mesh networks

Subject Areas : Majlesi Journal of Telecommunication Devices

Mahdieh Sasan ¹ , Farhad Faghani ²

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Received: 2015-09-06 Accepted : 2015-10-13 Published : 2015-11-01

Keywords: en,

Abstract :

Load balancing can be used to extend the lifetime of a Mesh Network and thus reducing the traffic congestion and improving the network performance. Various approaches have been proposed for load balancing in WMN .This study takes a closer look at existing solutions with the application of clustering techniques to solve routing and congestion control problems because it offers scalability and enhances the availability of network and reduced overheads. The nature of the problem of clustering is NP-hard and using meta-heuristic and evolutionary algorithms can build stable and relatively efficient clusters. This paper proposes a new clustering algorithm method in WMN networks and divide the network into k clusters to manage the load in small scale and hence to reduce the overall load of WMNs. This algorithm is a centralized method and it is designed on the basis of an imperialist competitive algorithm (ICA). A WMN is divided into multiple clusters for load control and each gateway served even number of node. A cluster head estimates traffic load in its cluster. As the estimated load gets higher, the cluster head increases the routing metrics of the routes passing through the cluster. The simulation results show that the performance of the WMNs is improved with the proposed clustering method.

References:

[1] A. Adekiigbe, K. Abu-Bakar, and O. S. Ogunnusi, “A Review of Cluster-Based Congestion Control Protocols in Wireless Mesh Networks,” Int. J. Comput. Sci., vol. 8, no. 4, pp. 42–50, 2011.

[2] J. J. Galvez, P. M. Ruiz, and A. F. G. Skarmeta, “A feedback-based adaptive online algorithm for multi-gateway load-balancing in wireless mesh networks,” in World of Wireless Mobile and Multimedia Networks (WoWMoM), 2010 IEEE International Symposium on a, 2010, pp. 1–9.

[3] I. Shayeb, “A survey of clustering schemes for Mobile Ad-Hoc Network (MANET),” Am. J. …, 2011.

[4] R. Agarwal and D. M. Motwani, “Survey of clustering algorithms for MANET,” vol. 1, no. 2, pp. 98–104, 2009.

[5] D. Turgut, “Optimizing Clustering Algorithm in Mobile Ad hoc Networks Using Genetic Algorithmic Approach University of Central Florida.”

[6] E. Atashpaz-Gargari and C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition,” in Evolutionary computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 4661–4667.

[7] T. Niknama, E. T. Fard, N. Pourjafarian, and A. Rousta, “An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering,” Eng. Appl. Artif. Intell., vol. 24, no. 2, pp. 306–317, 2011.

[8] E. Atashpaz Gargari and others, “A novel approach for PID controller design in MIMO distillation column process,” Proceeding IEEE CEC 2008, within IEEE WCCI 2008, pp. 1929–1934, 2008.

[9] H. Cheng, S. Yang, and J. Cao, “Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks,” no. Icnc, pp. 1171–1176, 2012.

[10] M. Buvana, M. Suganthi, and K. Muthumayil, “Novel architecture for cluster based service Discovery and load balancing in Mobile Ad hoc Network,” 2011 Third Int. Conf. Adv. Comput., pp. 292–297, 2011.

[11] F. Faghani and G. Mirjalily, “Shortcut Switching Strategy in Metro Ethernet networks,” Comput. Commun., vol. 34, no. 8, pp. 1022–1032, 2011.