# Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network
## 2 Literature Study  
Several articles have been devoted to the compilation of algorithms. In this section, we provide an overview of the previous literature and an analysis of its principles and technical limitations. **Firstly**, we explain the rough set clustering models that have been proposed for different applications. [PERSON] [8] proposed a new fuzzy set model to make decisions by the approximate inclusion of sets. The dataset is divided into upper and lower approximations. The rule decision can be implemented based on the upper or lower approximation depending on the application, and the boundary region can also be selected. Classical rough sets have the problems of difficulty dealing with real-value data and a low fault-tolerance level. The low tolerance level problem was solved in [9] by proposing a neighborhood rough-sets model to create variable-precision neighborhood approximation sets and positive regions, so the rough set becomes a dynamic model. The dynamic rough set model with the fuzzy scheme was used in [10] to make a more efficient rough set clustering system. This scheme enhances the rough set and implements it in a VANET dataset as a CH selection model. The CH selection model is created based on the node transmission range, and the vehicle is defined in a true cluster. Regarding MANET applications, the authors of [11] proposed a rough set calibration scheme based on the node energy to classify the route and select the best route as an energy-efficient routing technique. The rough set calibration scheme ultimately makes use of episodic association for each parameter, including distance and energy. A new rough-set style was used in [12] to create a MANET clustering algorithm, and the main parameters for assigning the nodes to a true cluster were energy, delay, and bandwidth. The \begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Definition** \\ \hline CBL & Chain-Branch-Leaf \\ CG & Covering Group \\ CH & Cluster Head \\ CM & Cluster Member \\ CRS & Covering Rough Set \\ DB & Davies-Bouldin Index \\ DI & Dunn Index \\ DSRC & Dedicated Short-Range Communication \\ MANET & Mobile Ad Hoc Network \\ ML & Machine Learning \\ MPR & Multi-Point Relay Node \\ NSC & Number of Status Changes \\ OK-Means & Overlapping K-Means \\ OLSR & Optimized Link State Routing Protocol \\ RMSE & Root Mean Square Error \\ RSV & Road Side Unit \\ SC & Silhouette Coefficient \\ SUMO & Simulation of Urban Mobility \\ UAV & Unmanned Aerial Vehicle \\ V2I & Vehicle-to-Infrastructure Communications \\ V2V & Vehicle-to-Vehicle Communications \\ VANET & Vehicular Ad Hoc Network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of notations and acronyms. route classification model can classify the routes in a wireless network. The experimental route-classification results illustrate that this model has high accuracy compared with other algorithms. Regarding healthcare data analysis, a CRS was used in [13] to enhance prediction, the initial identification of sicknesses, and disease classification by applying rough-set-based pattern classification techniques for healthcare data. For the diagnosis of diseases in different patients, the covering-based rough set classification provided more effective results than the delicate pattern classifier model. A novel neighborhood rough set classification approach was presented in [14] to deal with medical datasets. Five benchmarked medical datasets were used to study the impact of the neighborhood rough set classification approach on decision making. In [15], the dimension reduction of clusters was carried out based on a rough set to decrease the feature set of a decision system and improve the cluster performance. The application of the rough-set technique in VANETs represented a research gap, so the proposed model in this paper uses a CRS to create a covering group based on an effective VANET parameter to improve the K-Means clustering algorithm.