ISSN: 0973-7510

E-ISSN: 2581-690X

M.Viju Prakash1, M. Kaliappan2 and B. Paramasivan2
1St. Xavier’s Catholic College of Engineering, Nagercoil, Tamil Nadu, India,
National Engineering College, Kovilpatti, Tamilnadu, India.
J Pure Appl Microbiol. 2015;9(Spl. Edn. 2):655-665
© The Author(s). 2015
Received: 10/07/2015 | Accepted: 03/09/2015 | Published: 30/11/2015
Abstract

Mobile Ad hoc Network (MANET) is a kind of self configuring networks. MANET has characteristics of topology dynamics due to factors such as energy conservation and node movement that leads to dynamic load balanced clustering problem (DLBCP). Load balancing and reliable data transfer between all the nodes are essential to prolong the lifetime of the network. MANET can also be partitioned into clusters for maintaining the network structure. Generally, Clustering is used to reduce the size of topology and to accumulate the topology information. It is necessary to have an effective clustering algorithm for adapting the topology change. In this, we used energy metric in Genetic Algorithm (GA) to solve the DLBCP. It is important to select the energy efficient cluster head for maintaining the cluster structure and balance the load effectively. In this work, we used dynamic genetic algorithms such as Elitism based Immigrants Genetic algorithm (EIGA) and Memory Enhanced Genetic Algorithm (MEGA) to solve DLBCP. These schemes, select an optimal cluster head by considering the distance and energy parameters. We used EIGA to maintain the diversity level of the population and MEGA to store the old environments into the memory. It promises the energy efficiency of the entire cluster structure to increase the lifetime of the network. Experimental results show that the proposed schemes increases the network lifetime and reduces the total energy consumption. The simulation results show that MEGA gives a better performance than EIGA in terms of load balancing.

Keywords

Adhoc Networks, Clustering, Dynamic load balancing, Elitism based Immigrant Genetic algorithm, Memory Enhanced Genetic Algorithm

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