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Research Article |

Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Author(s): Praveenkumar R1*, Kirthika2, Durai Arumugam3 and Dinesh4

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 2

Publisher : FOREX Publication

Published : 19 June 2023

e-ISSN : 2347-470X

Page(s) : 426-433

DOI: https://doi.org/10.37391/IJEER.110224

Abstract

Wireless sensor networks (WSN) keep developing in recent days concerning the self-covered network, self-healing network, and association of system component circuit selections that enable the implementation process. Wireless sensor network lifetime stabilization is essential to providing a higher quality experience to consumers. The wireless sensor network is associated with classifiers that keep learning the data pattern and further modify the cluster selection to produce dynamic results. The presented system is focused on creating an efficient wireless sensor network in which cluster head selection is dynamically programmed to improve the life span of the devices. The energy utilized by each node is pre-programmed with random assignments. The values are configured by the machine learning techniques to improve the hop death. The models developed using the parameters help project energy consumption. The proposed system considers a support vector machine (SVM), and the Gaussian regression process (GRP) enabled the comparative study of lifespan analysis and support systems to make cluster selection efficient. The proposed model is used to test the current selection of cluster heads using a support rectangle machine and further modify the regression process using the Gaussian regression model. Evaluation of network lifetime and flexibility obtained in cluster selection.

Keywords: Wireless sensor networks, Machine learning, Clustering Scheme.




Praveenkumar R*, Easwari Engineering College, Ramapuram, Chennai, India; Email: rpjcspraveen@gmail.com

Kirthika, Easwari Engineering College, Ramapuram, Chennai, India; Email: jayaramankirthika8@gmail.com

Durai Arumugam, Easwari Engineering College, Ramapuram, Chennai, India; Email: duraiarumugam36@gmail.com

Dinesh, Easwari Engineering College, Ramapuram, Chennai, India; Email: dinesh.v@eec.srmrmp.edu.in

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Praveenkumar R, Kirthika, Durai Arumugam and Dinesh (2023), Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection. IJEER 11(2), 426-433. DOI: 10.37391/IJEER.110224.