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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-8915121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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