Open Access

ARTICLE

Hybrid BWO-IACO Algorithm for Cluster Based Routing in Wireless Sensor Networks

R. Punithavathi¹, Chinnarao Kurangi², S. P. Balamurugan³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, K. Shankar^6,*

1 Department of Information Technology, M. Kumarasamy College of Engineering, Karur, 639113, India
2 Department of Computer Science, Pondicherry University, Puducherry, 605014, India
3 Department of Computer and Information Science, Faculty of Science, Annamalai University, Chidambaram, 608002, India
4 Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, 117997, Russia
5 Department of Logistics, State University of Management, Moscow, 109542, Russia
6 Department of Computer Applications, Alagappa University, Karaikudi, 630001, India

* Corresponding Author: K. Shankar. Email:

Computers, Materials & Continua 2021, 69(1), 433-449. https://doi.org/10.32604/cmc.2021.018231

Received 01 March 2021; Accepted 02 April 2021; Issue published 04 June 2021

Abstract

Wireless Sensor Network (WSN) comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region. As the nodes in WSN operate on inbuilt batteries, the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime. To enhance energy efficiency and network longevity, clustering and routing techniques are commonly employed in WSN. This paper presents a novel black widow optimization (BWO) with improved ant colony optimization (IACO) algorithm (BWO-IACO) for cluster based routing in WSN. The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads (CHs). The BWO algorithm derives a fitness function (FF) using five input parameters like residual energy (RE), inter-cluster distance, intra-cluster distance, node degree (ND), and node centrality. In addition, IACO based routing process is involved for route selection in inter-cluster communication. The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm (KHA). The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network. The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime. The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects. A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.

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Keywords

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