Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network
نویسندگان
چکیده
Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc (VANET) clustering techniques for different applications. The effectiveness model is most important challenge. K-Means algorithm an effective multi-clusters that can be used in VANETs. problems with concern selection a suitable number clusters, creation highly reliable cluster, and achieving high similarity within cluster. To address these problems, novel method combining covering rough set (RK-Means) was this paper. Firstly, RK-Means creates multi-groups vehicles using based on parameters. Secondly, K-value-calculating computes optimal clusters. Finally, classical applied create vehicle clusters each group. datasets work were imported from Simulation Urban Mobility (SUMO), representing two highway scenarios, high-density low-density. Four evaluation indexes, namely, root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, Dunn index (DI), directly test evaluate results clustering. process implemented RK-Means, K-Means++, OK-Means models. result compression showed had cluster similarity, greater reliability, reductions 32.5% 24.2% compared respectively.
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ژورنال
عنوان ژورنال: Mathematics
سال: 2022
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math10244720