Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network
Many 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...
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my.uniten.dspace-266302023-05-29T17:35:58Z Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network Abdulrazzak H.N. Hock G.C. Mohamed Radzi N.A. Tan N.M.L. Kwong C.F. 57210449807 16021614500 57218936786 24537965000 56430628700 Many 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. Final 2023-05-29T09:35:58Z 2023-05-29T09:35:58Z 2022 Article 10.3390/math10244720 2-s2.0-85144656009 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144656009&doi=10.3390%2fmath10244720&partnerID=40&md5=c80827b4f8aaa0211e13a146e517c9ba https://irepository.uniten.edu.my/handle/123456789/26630 10 24 4720 All Open Access, Gold MDPI Scopus |
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Many 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. |
| author2 |
57210449807 |
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57210449807 Abdulrazzak H.N. Hock G.C. Mohamed Radzi N.A. Tan N.M.L. Kwong C.F. |
| format |
Article |
| author |
Abdulrazzak H.N. Hock G.C. Mohamed Radzi N.A. Tan N.M.L. Kwong C.F. |
| spellingShingle |
Abdulrazzak H.N. Hock G.C. Mohamed Radzi N.A. Tan N.M.L. Kwong C.F. Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network |
| author_sort |
Abdulrazzak H.N. |
| title |
Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network |
| title_short |
Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network |
| title_full |
Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network |
| title_fullStr |
Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network |
| title_full_unstemmed |
Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network |
| title_sort |
modeling and analysis of new hybrid clustering technique for vehicular ad hoc network |
| publisher |
MDPI |
| publishDate |
2023 |
| _version_ |
1806426568943206400 |
| score |
13.211869 |