Enhancing energy efficient dynamic load balanced clustering protocol using Dynamic Genetic Algorithm

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303

Enhancing energy efficient dynamic load balanced clustering protocol using Dynamic Genetic Algorithm in MANET 1

T.Dhivya1

2

M.E communication and Networking National Engineering College, Kovilpatti, dhivyathiru17@email.com

M.Kaliappan2 Assistant Professor, IT Department

National Engineering College, Kovilpatti kalsrajan@yahoo.co.in

Abstract— Mobile Ad hoc Network (MANET) is a kind of self configuring and self describing wireless ad hoc 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). It is necessary to have an effective clustering algorithm for adapting the topology change. Generally, Clustering is mainly used to reduce the topology size. In this, we used load balance and energy metric in 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. Elitism based Immigrants Genetic algorithm (EIGA) and Memory Enhanced Genetic Algorithm (MEGA) are used to solve DLBCP. These schemes will select the optimal cluster head by considering the parameters includes distance and energy. We used EIGA to maintain the diversity level of the population and memory scheme (MEGA) to store the old environments into the memory. It promises the energy efficiency for the entire cluster structure to increase the lifetime of the network. The experimental results show that the proposed schemes increases the network life time and reduces the energy consumption. Index Terms— MANET, DLBCP, Genetic Algorithm (GA), Dynamic GA, Immigrants scheme, Memory scheme. ——————————  ——————————

1 INTRODUCTION In Mobile Ad hoc Network (MANET), it has more number of nodes and its topology changes randomly and dynamically due to its characteristics [2]. So clustering of nodes is very essential in MANET. The information about the nodes and links are stored within each cluster. Due to the random movement of nodes in MANET, Scalability problem arises. So Clustering [3] is the most efficient one to solve this problem in MANETs. And also clustering is useful for efficient routing and for topology control. For effective cluster structure, the selection of cluster head is more important in a dynamically changing environment. Genetic Algorithm (GA) techniques is used to find the best and optimal cluster head to each cluster. Genetic Algorithm is an optimization heuristics technique to find the optimal solution. This technique will select the best one from the group of members by using the operations like selection, mutation and crossover. Genetic Algorithm is not a deterministic technique so it is used in the dynamic environments to select the possible solutions. In the genetic algorithm,each one represents a possible solution. In this paper, this technique is used to select the cluster head dynamically .Cluster heads are selected based on the distance and energy of each node in network. Due to the movement of nodes in the MANET, it leads to Dynamic Load Balanced Clustering Problem (DLBCP). Dynamic genetic algorithm is used to solve the DLBCP. In a Dynamic genetic Algorithm, the series of Dynamic schemes are used to manage the dynamic network environments and to maintain the populations. The dynamic GA schemes include immigrants schemes and memory related schemes are mainly used to maintain the population and to store the information about an environment respectively. The contributions of this work are summarized as follows.

We designed a Dynamic optimization problem into dynamic load balanced clustering problem (DLBCP). So Dynamic GA is used to solve this problem

Cluster head selection is done by using the genetic Algorithm operations such as selection, fitness function, mutation and crossover. In this work we have considered the parameters like distance and energy to select the cluster head and to balance the load. We used two dynamic schemes like Elitism based Immigrants Genetic Algorithm (EIGA) and Memory Enhanced Genetic Algorithm (MEGA) to solve this problem. We examine their performances on the DLBCP. The results show that MEGA out performs the EIGA.

The rest of this paper contains the following sections. In section 2 the related works are discussed. The Network model and DLBCP problem are discussed in the section 3. In section 4 we had presented the pursued GA for the static load balanced clustering problem. The dynamic GA schemes are described and also it is used to solve the DLBCP in the section 5. In section 6 the experimental analysis and the performance of the networks are evaluated. Conclusion of this paper was presented in the section 7

2 RELATED WORKS

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Priyanka Goyal et al. [1] described the fundamental problems of ad hoc network by giving its related research background including the concept, features, status, and vulnerabilities of MANET. Due to severe challenges, the special features of


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