Open Access

ARTICLE

A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

C. Ram Kumar^1,*, K. Murali Krishna², Mohammad Shabbir Alam³, K. Vigneshwaran⁴, Sridharan Kannan⁵, C. Bharatiraja⁶

1 Department of Electronics and Communication Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, 641048, India
2 Department of Electronics and Communication Engineering, Vignan’s Institute of Information Technology, Duvvada, Visakhapatnam, 530049, India
3 Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jizan, 45142, Kingdom of Saudi Arabia
4 Department of Electronics and Communication Engineering, K.Ramakrishnan College of Engineering, Tiruchirappalli, 621112, India
5 Department of Computer Science and Engineering, JKK Munirajah College of Technology, Erode, 638506, India
6 Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, 603203, India

* Corresponding Author: C. Ram Kumar. Email:

Computer Systems Science and Engineering 2022, 43(1), 259-273. https://doi.org/10.32604/csse.2022.023477

Received 09 September 2021; Accepted 10 October 2021; Issue published 23 March 2022

Abstract

The Wireless Sensor Networks (WSN) is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission. The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head. The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network. The proposed model is a hybridization of Glowworm Swarm Optimization (GSO) and Artificial Bee Colony (ABC) algorithm for the better identification of cluster head. The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35% better value and reduction of time complexity upto 1.46%. Above all, the proposed model is 16% ahead of alive node count when compared with the existing methodologies.

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Keywords

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