# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 3 Materials and Methods
### Construction of Zones
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Parameters** & **Definition** & **Description** \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{\(RSU\)} & Roadside unit to process the request and communication devices & Roadside units are responsible for communicating with the central manager and vehicles. These are fixed stations on the end of the highways to provide content and other control facilities to the VANET environment. \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{\(CT\)} & Content storage in popularity order (ascending or descending order) & The content is stored with the popularity order of the network resources, enhancing the access and privileges mechanism to control and communicate among these challenges. \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{\(C_{zi}\)} & Congestion of zone & The congestion in every zone is balanced through the proposed techniques. The congestion is related to the content and requests for content placement and enhancements. \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{\(CH\)} & Cluster heads for zones & Every zone holds a selected cluster head, the cluster’s part, and makes effective cluster collaborations to discuss and provide the cluster-based implementation policies. The discussed results provide effective zone-based content pre-caching and enhancement strategies. \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{\(X_{md}\)} & \multirow{2}{*}{Zone radius} & Every zone holds a radius that consumes the zone area and provides the system enhancement features to collect and discuss the resulting privileges. \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{\(ML\)} & Machine learning algorithms for vehicles & We adopted a machine learning vehicles-based approach to handle the content congestion and enhanced the features for the collaborative workings. \\ \hline \multirow{2}{*}{10} & \multirow{2}{*}{Content cache} & Content caching for passing from machine learning & The content caching is passed through machine learning approaches and provides the collaborative work methodology for content cache placement. \\ \hline \multirow{2}{*}{11} & \multirow{2}{*}{\(V2V_{com}\)} & Communication points from vehicle-to-vehicle & The communication point from the vehicular addresses provides the collective methodology for the results and discussions to effectively elaborate and provide the working methodologies. \\ \hline \multirow{2}{*}{12} & \multirow{2}{*}{\(V2I_{com}\)} & Communication points from vehicle to infrastructure & The vehicle infrastructure contains an appropriate communication point that shows the collection of infrastructure movements and provides enhancement strategies for infrastructure-based content placement schemes. \\ \hline \multirow{2}{*}{13} & \multirow{2}{*}{\(R_{r}\)} & \multirow{2}{*}{Requested content of size r} & The content requested by the consumer node is of size r, where r is the threshold value set in the algorithm to provide effective caching placement and strategies implementations. \\ \hline \multirow{2}{*}{14} & \multirow{2}{*}{\(D_{r}\)} & Delivered content of size r & The content which is delivered to the requested vehicles is of size r. \\ \hline \multirow{2}{*}{15} & \multirow{2}{*}{\(Req_{data}\)} & Requested content with data requested & This shows the data and content request from the requested node toward the content placement and enhancement strategies. \\ \hline \multirow{2}{*}{16} & \multirow{2}{*}{\(DM_{Nolde}^{Z}\)} & Data rate among manager nodes and vehicles & The data rate for the manager node and vehicles shows the data delivered among these nodes for proper implementation. \\ \hline \multirow{2}{*}{17} & \multirow{2}{*}{\(Z_{table}\)} & Intrazone table & This table shows the communication among different zones and their coordination among them. \\ \hline \hline \end{tabular}
\end{table}