# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 3 Materials and Methods  
### Proposed Zone-Based VANET Congestion Control Architecture Using ML  
This section briefly describes the research parameters and explains our proposed system with pre-caching in VANET through a machine learning technique. Figure 3 shows proposed model for Zone-based congestion control architecture. Initially, we discuss the construction of the zones through the clustering technique with parameters to select vehicles and define the threshold area with defined properties for individual zone. Roadside units (RSUs) and base station/content manager are added to collect the data through the zone-wise allocation of the vehicular adaptation [30]. This means pre-cached content initially fetched from RSUs. If an RSU cannot provide the content, then the request is forwarded to the base station/central manager through the RSU. Each zone is responsible for collecting RSUs. After that, we adopt machine learning to train the system to collect only urban \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**S. No.** & **Author** & **Methodology** & **Parameters** & **Contributions** & **Limitations** \\ \hline \hline
1 & [PERSON] et al. [16] & The pre-caching node selection algorithm & prediction accuracy and average delay & Effectively pre-cache the content & Overhead involved when loading the content to the node \\ \hline
2 & [PERSON] et al. [17] & Name-based content caching & Network delay and performance & Handle the content normal caching & It does not work on cache prediction accuracy \\ \hline
3 & [PERSON] et al. [18] & Stackelberg Game Approach & Congestion and content quantity & Overcome network congestion & Does not work on the network throughout, and network performance metrics \\ \hline
4 & [PERSON] et al. [19] & Two-layer distributed content caching & Caching capacity, transmission rates & Performance analysis & Congestion handling, throughput, and network performance \\ \hline
5 & [PERSON] et al. [20] & Game-based caching incentive scheme & Content caching & Enhance content caching results & The cost of caching is very high \\ \hline
6 & [PERSON] et al. [21] & Hidden Markov model-based content pre-caching & Delivery ratio, hop count, and network latency values, ML technique to enhance the proposed technique & Enhance the caching scheme for the efficiency of the proposed technique & ML technique for the efficiency of the content caching technique \\ \hline
7 & [PERSON] et al. [22] & RSU caching and vehicular pre-caching & Content pre-caching delivery time, load balancing & Caching performance, delivery time & It does not consider cache prediction accuracy, average delay, or cache hit ratio \\ \hline
8 & [PERSON] et al. [23] & Integrated networking approach & Deep reinforcement learning approach & Content caching and system enhancements & No discussion about congestion handling and network performance \\ \hline
9 & [PERSON] et al. [25] & Joint optimization scheme of content caching & ML-based content caching, joint-implemented strategy, policy for efficiency & Clusters or zones which are not effective & There is no use of clusters or zones or throughput \\ \hline
10 & [PERSON] et al. [27] & Mobility-aware edge caching approach & Handling through network performance and measurement & Large-scale mobility aware cost estimation model & Does not find content pre-caching strategies and every time post content caching policies \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common limitations of the related works from the literature. vehicles. The data are collected through zones/clusters and forwarded to a trained machine learning model to predict cache hit ratio throughput average delay in putting the cached content through the machine learning mechanism. The intelligent CNN-based machine learning model predicts that only sensitive and safe messages handle content caching mechanisms.