# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 1 Introduction
As every moving environment causes frequent service disruption, other related parameters cause the delay of the service and provide a significant enhancement for the dynamic coverage area. The delay in the content request may also cause server performance degradation. Pre-caching is one of the main solutions that caches the valuable content after pre-caching and enhances the working of wireless sensors to control and provide effective parametric values. The content request and response process may enhance the complete working functionality of the provided content request and response parameters. Handling all the contents and requests may cause service delays and content handling efficiencies [9]. The data produced by urban vehicles continue in nature, so predictions about road safety and traffic control are difficult. The speeds of an urban area's vehicles are fast, so pre-time prediction of road conditions and accidents can save more disasters. Thus, designing an efficient data pre-cache prediction is one of the more efficient techniques to resolve these issues. The proposed protocols inform vehicles about interesting safety events and efficiently overcome disasters by early prediction of the results. In VANET, content caching consists of different types. These types are not according to the content storage but the provided mechanism. The content caching stores the content on RSU, Cluster Head, or directly on the vehicular node. There are some techniques in which the content is stored sequentially. These techniques are presented only to download the cached content sequentially. On the other hand, some techniques require content on-demand bases. These techniques efficiently place the content on demand whenever and wherever a vehicular node requires content. The BS, CH, or RSU cache the content on demand based. The last type of caching is content pre-caching approaches. These are more efficient than others because they predict early events from the content vehicles and provide an effective and collaborative mechanism for content pre-caching techniques [10]. There are still some types of techniques that are most relevant to enhancing the proposed techniques. Content pre-caching is the most efficient technique with respect to all other techniques to provide effective and reliable security and other details to provide the most efficient mechanism to support effectiveness. Adapting automatic behavior and working are significant achievements for VANET communication like machine learning. Machine learning is an attractive and emerging technique used for multiple application domains. An important technique is the VANET, wherein machine learning-based techniques are helpful in addressing different problem domains and discussing the relevant application scenarios. Nowadays, ML is applied in almost every area of VANET, like security, caching, training, routing, congestion handling, etc. Specially customized datasets are required to promote machine learning adoption in VANET. Figure 2 shows the ML models and algorithms. A typical model for the traditional ML consists of three phases: (1) the training phase, which takes raw data and pre-processes it to extract the features. The  
Figure 1: VANET structure for AU, OBU, and RSU [2]. features are input into the ML model to learn patterns and classes of the data; (2) the testing phase, where the ML model tests a new set of data for classification based on its learning experience from the training phase; (3) the prediction phase, also known as the evaluation phase, where the working efficiency of an ML model is evaluated based on quality metrics (such as accuracy, false-positives, false-negatives, and so on).