Open Access

ARTICLE

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

by S. Siamala Devi¹, Chandrakala Kuruba², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

1 Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, 641042, India
2 Department of Computer Science and Engineering, Vignan Nirula Institute of Technology and Science, Guntur, 522009, India
3 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea
4 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
5 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA

* Corresponding Author: Yunyoung Nam. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1873-1887. https://doi.org/10.32604/iasc.2023.025504

Received 26 November 2021; Accepted 10 January 2022; Issue published 19 July 2022

Abstract

Wireless sensor networks (WSNs) are projected to have a wide range of applications in the future. The fundamental problem with WSN is that it has a finite lifespan. Clustering a network is a common strategy for increasing the lifetime of WSNs and, as a result, allowing for faster data transmission. The clustering algorithm’s goal is to select the best cluster head (CH). In the existing system, Hybrid grey wolf sunflower optimization algorithm (HGWSFO)and optimal cluster head selection method is used. It does not provide better competence and output in the network. Therefore, the proposed Hybrid Grey Wolf Ant Colony Optimisation (HGWACO) algorithm is used for reducing the energy utilization and enhances the lifespan of the network. Black hole method is used for selecting the cluster heads (CHs). The ant colony optimization (ACO) technique is used to find the route among origin CH and destination. The open cache of nodes, transmission power, and proximity are used to improve the CH selection. The grey wolf optimisation (GWO) technique is the most recent and well-known optimiser module which deals with grey wolves’ hunting activity (GWs). These GWs have the ability to track down and encircle food. The GWO method was inspired by this hunting habit. The proposed HGWACO improves the duration of the network, minimizes the power consumption, also it works with the large-scale networks.The HGWACO method achieves 25.64% of residual energy, 25.64% of alive nodes, 40.65% of dead nodes also it enhances the lifetime of the network.

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Keywords

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