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Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor no...

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Autores principales: Suganthi, Sundararaj, Umapathi, Nagappan, Mahdal, Miroslav, Ramachandran, Manickam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915121/
https://www.ncbi.nlm.nih.gov/pubmed/35270885
http://dx.doi.org/10.3390/s22051736
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author Suganthi, Sundararaj
Umapathi, Nagappan
Mahdal, Miroslav
Ramachandran, Manickam
author_facet Suganthi, Sundararaj
Umapathi, Nagappan
Mahdal, Miroslav
Ramachandran, Manickam
author_sort Suganthi, Sundararaj
collection PubMed
description Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study’s outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay.
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spelling pubmed-89151212022-03-12 Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network Suganthi, Sundararaj Umapathi, Nagappan Mahdal, Miroslav Ramachandran, Manickam Sensors (Basel) Article Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study’s outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay. MDPI 2022-02-23 /pmc/articles/PMC8915121/ /pubmed/35270885 http://dx.doi.org/10.3390/s22051736 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Suganthi, Sundararaj
Umapathi, Nagappan
Mahdal, Miroslav
Ramachandran, Manickam
Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
title Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
title_full Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
title_fullStr Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
title_full_unstemmed Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
title_short Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
title_sort multi swarm optimization based clustering with tabu search in wireless sensor network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915121/
https://www.ncbi.nlm.nih.gov/pubmed/35270885
http://dx.doi.org/10.3390/s22051736
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