Dynamic Load Balanced Clustering using Elitism based Random Immigrant Genetic Approach for Wireless Sensor Networks

Acta Graphica

Pitchai, K. Mohaideen¹ 

[1] Department of Computer Science and Engineering, National Engineering College, Kovilpatti–628503, Tamilnadu, India

关键词: Genetic Algorithm;    Elitism;    Load Balanced Clustering;    Random Immigrants;    Wireless Sensor Network;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: Acta Graphica Publishers

【 摘 要 】

Wireless Sensor Network (WSN) consists of a large number of small sensors with restricted energy. Prolonged network lifespan, scalability, node mobility and load balancing are important needs for several WSN applications. Clustering the sensor nodes is an efficient technique to reach these goals. WSN have the characteristics of topology dynamics because of factors like energy conservation and node movement that leads to Dynamic Load Balanced Clustering Problem (DLBCP). In this paper, Elitism based Random Immigrant Genetic Approach (ERIGA) is proposed to solve DLBCP which adapts to topology dynamics. ERIGA uses the dynamic Genetic Algorithm (GA) components for solving the DLBCP. The performance of load balanced clustering process is enhanced with the help of this dynamic GA. As a result, the ERIGA achieves to elect suitable cluster heads which balances the network load and increases the lifespan of the network.

【 授权许可】

CC BY-ND