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Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network

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dc.contributor.authorAbdulrazzak H.N.en_US
dc.contributor.authorHock G.C.en_US
dc.contributor.authorMohamed Radzi N.A.en_US
dc.contributor.authorTan N.M.L.en_US
dc.contributor.authorKwong C.F.en_US
dc.contributor.authorid57210449807en_US
dc.contributor.authorid16021614500en_US
dc.contributor.authorid57218936786en_US
dc.contributor.authorid24537965000en_US
dc.contributor.authorid56430628700en_US
dc.date.accessioned2023-05-29T09:35:58Z
dc.date.available2023-05-29T09:35:58Z
dc.date.issued2022
dc.description.abstractMany researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies�Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively. � 2022 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4720
dc.identifier.doi10.3390/math10244720
dc.identifier.issue24
dc.identifier.scopus2-s2.0-85144656009
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85144656009&doi=10.3390%2fmath10244720&partnerID=40&md5=c80827b4f8aaa0211e13a146e517c9ba
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26630
dc.identifier.volume10
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleMathematics
dc.titleModeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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