# Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network  
[PERSON]  
1 Institute of Power Engineering (IPE), Universiti Tenaga National (UNITEN), Kajang 43000, Malaysia 1  
[PERSON]  
1 Institute of Power Engineering (IPE), Universiti Tenaga National (UNITEN), Kajang 43000, Malaysia 1  
[PERSON]  
1 Institute of Power Engineering (IPE), Universiti Tenaga National (UNITEN), Kajang 43000, Malaysia 1  
[PERSON]  
1 Institute of Power Engineering (IPE), Universiti Tenaga National (UNITEN), Kajang 43000, Malaysia 1  
collecting neighboring nodes into clusters [4]. Different algorithms have different rules for creating clusters. The main VANET clustering scheme is shown in Figure 1. The connections between vehicles in the same cluster are called intra-cluster connections, and the connection between any two individual clusters is called an inter-cluster connection. The vehicles in a cluster are either cluster members (CMs), i.e., ordinary nodes, or the cluster head (CH), i.e., the cluster leader node.  
VANET clustering techniques depend on the network topology. In a highway scenario, the most important parameters are the vehicle location, speed, and direction. In an urban scenario, the steering angle should be added as an important parameter. The cluster stability is calculated using either the average speed rate or the cluster changing rate. In [5], the number of status changes (NSC) of a vehicle during its participation in the network system was taken as the relationship between its speed and the vehicle transmission range. The cluster changing rate was computed in [6; 7] to improve the cluster stability and select an optimal number of clusters. Converging cluster nodes and diverging the centroids of different clusters are two clustering algorithm challenges. The clustering algorithm proposed in this paper, RK-Means, creates a more stable clustering model with constraints on the average vehicle speed, therefore minimizing the intra-cluster distance (converging cluster nodes) and maximizing the inter-cluster distance (cluster divergence). The simulation results indicated that RK-Means outperformed the benchmark clustering models in the literature in terms of the evaluation by the unsupervised indexes RMSE, DB, DI, and SC, and cluster stability. The minimum value of standard deviation indicated that the proposed model has a small speed variant and that its cluster lifetime is higher than those of other algorithms.  
The main contributions and innovations of this paper can be summarized as follows:  
* A new K-Means clustering model based on a covering rough set is proposed to improve the cluster stability with minimum vehicle speed differences within clusters.
* The hybrid algorithm includes a new K-value calculation model to randomly enhance the attribution of data points and select an optimal number of clusters based on the minimum within-cluster error rate and vehicle coverage range.
* Five unsupervised evaluation indexes (RMSE, DB, DI, SC, and the standard deviation of speed) were used directly to evaluate and analyze the results of the clustering.  
Figure 1: VANET clustering scheme.  
* Comparative tests were designed and executed to show the effectiveness of RK-Means. We selected two highway topology scenarios (high-density and low-density) and eight datasets, with constraints on the simulation time and vehicle density.  
The rest of the paper is structured as follows: Section 2 summarizes the previous related studies. Section 3 introduces the system model. In Section 4, the evaluation model and indexes are described. Section 5 presents the simulation results and evaluates the performance of the proposed algorithm in comparison with two other algorithms based on the explained indexes. Finally, the conclusions and future research are presented in Section 6. The notations and acronyms used in this paper are shown in Table 1.