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Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning

The Wireless Sensor Network is a network formed in areas human beings cannot access. The data need to be sensed by the sensor and transferred to the sink node. Many routing protocols are designed to route data from a single node to the sink node. One of the routing protocols is the hierarchical rout...

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Autores principales: Ramesh, S., Rajalakshmi, R., Dwivedi, Jaiprakash Narain, Selvakanmani, S., Pant, Bhaskar, Bharath Kumar, N., Fissiha Demssie, Zelalem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9392616/
https://www.ncbi.nlm.nih.gov/pubmed/35996654
http://dx.doi.org/10.1155/2022/5393251
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author Ramesh, S.
Rajalakshmi, R.
Dwivedi, Jaiprakash Narain
Selvakanmani, S.
Pant, Bhaskar
Bharath Kumar, N.
Fissiha Demssie, Zelalem
author_facet Ramesh, S.
Rajalakshmi, R.
Dwivedi, Jaiprakash Narain
Selvakanmani, S.
Pant, Bhaskar
Bharath Kumar, N.
Fissiha Demssie, Zelalem
author_sort Ramesh, S.
collection PubMed
description The Wireless Sensor Network is a network formed in areas human beings cannot access. The data need to be sensed by the sensor and transferred to the sink node. Many routing protocols are designed to route data from a single node to the sink node. One of the routing protocols is the hierarchical routing protocol, which passes on the sensed data hierarchically. The Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the hierarchical methods in which communication happens in two steps: the setup phase and the steady-state phase. The efficiency of the LEACH has to be optimized to improve the network lifetime. Therefore, the k-means clustering algorithm, which comes under the unsupervised machine learning method, is incorporated with the LEACH algorithm and has shown better results. But the selection of cluster head needs to improvise because it will transfer the summed-up data to the sink node, so it is to be efficient enough. So, this paper proposes the modified k-means algorithm with LEACH protocol for optimizing the Wireless Sensor Network. In the modified k-means algorithm, the weight of the cluster head is tested and elected, and the clusters are formed using the Euclidean distance formula. The proposed work yields 48.85% efficiency compared to the existing protocol. It is also proven that the proposed work showed more successful data transfer to the sink node. The cluster head selection process elects the more efficient node as the head with less failure rate. The proposed work optimistically balanced the whole network in terms of energy and successful data transfer.
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spelling pubmed-93926162022-08-21 Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning Ramesh, S. Rajalakshmi, R. Dwivedi, Jaiprakash Narain Selvakanmani, S. Pant, Bhaskar Bharath Kumar, N. Fissiha Demssie, Zelalem Comput Intell Neurosci Research Article The Wireless Sensor Network is a network formed in areas human beings cannot access. The data need to be sensed by the sensor and transferred to the sink node. Many routing protocols are designed to route data from a single node to the sink node. One of the routing protocols is the hierarchical routing protocol, which passes on the sensed data hierarchically. The Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the hierarchical methods in which communication happens in two steps: the setup phase and the steady-state phase. The efficiency of the LEACH has to be optimized to improve the network lifetime. Therefore, the k-means clustering algorithm, which comes under the unsupervised machine learning method, is incorporated with the LEACH algorithm and has shown better results. But the selection of cluster head needs to improvise because it will transfer the summed-up data to the sink node, so it is to be efficient enough. So, this paper proposes the modified k-means algorithm with LEACH protocol for optimizing the Wireless Sensor Network. In the modified k-means algorithm, the weight of the cluster head is tested and elected, and the clusters are formed using the Euclidean distance formula. The proposed work yields 48.85% efficiency compared to the existing protocol. It is also proven that the proposed work showed more successful data transfer to the sink node. The cluster head selection process elects the more efficient node as the head with less failure rate. The proposed work optimistically balanced the whole network in terms of energy and successful data transfer. Hindawi 2022-08-13 /pmc/articles/PMC9392616/ /pubmed/35996654 http://dx.doi.org/10.1155/2022/5393251 Text en Copyright © 2022 S. Ramesh et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ramesh, S.
Rajalakshmi, R.
Dwivedi, Jaiprakash Narain
Selvakanmani, S.
Pant, Bhaskar
Bharath Kumar, N.
Fissiha Demssie, Zelalem
Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning
title Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning
title_full Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning
title_fullStr Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning
title_full_unstemmed Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning
title_short Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning
title_sort optimization of leach protocol in wireless sensor network using machine learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9392616/
https://www.ncbi.nlm.nih.gov/pubmed/35996654
http://dx.doi.org/10.1155/2022/5393251
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