Open Access

ARTICLE

Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

Avishek Banerjee¹, Deepak Garg¹, Victor Das², Laxminarayan Sahoo³, Ira Nath⁴, Vijayakumar Varadarajan⁵, Ketan Kotecha^6,*

1 Department of Computer Science and Engineering, Bennett University, Greater Noida, 201310, India
2 Department of Information Technology, Asansol Engineering College, Asansol, 713305, India
3 Department of Computer and Information Science, Raiganj University, Raiganj, 733134, India
4 Department of Computer Science and Engineering, JIS College of Engineering, Kalyani, 741235, India
5 School of Computer Science and Engineering, University of New South Wales,Sydney, Australia
6 Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, India

* Corresponding Author: Ketan Kotecha. Email:

Computers, Materials & Continua 2022, 72(2), 3585-3600. https://doi.org/10.32604/cmc.2022.025233

Received 17 November 2021; Accepted 21 February 2022; Issue published 29 March 2022

Abstract

In this paper, the establishment of efficient Wireless Sensor Network (WSN) networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach (SMOWA) algorithm for solving a multi-objective problem. The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficient Wireless Sensor Network to minimize energy consumption. After that, the cluster head for each cluster has been selected with the help of the duty cycle. After configuring the WSN networks, the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks. Energy minimization, as well as the amount of day-saving, has been calculated for the different WSNs which has been configured through different deployment policies. The major finding of the research paper is to improve the durability of Wireless Sensor Network (i) applying different deployment strategies: (Random, S pattern and nautilus shell pattern), and (ii) using a new Meta-heuristic algorithm (SMOWA Algorithm). In this research, the lifetime of WSN has been increased to a significant level. To choose the best result set from all the obtained results set some constraints such as “equivalent distribution”, “number of repetitions”, “maximum amount energy storage by a node” has been set to an allowable range.

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Keywords

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