# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 3 Materials and Methods
### Construction of Zones
* The content is cached on every relevant vehicle using \(ZoR\) and \(RoF\). The event location and consequences are disseminated through safe messages and cached in every zone for future safety predictions. Equation (4) depicts this knowledge. \[R_{event}\leftarrow\ V_{ZoR}*\ \sum M_{ZoR}\] (4)
* Additionally, the content manager \(CM\) keeps track of every zone \(Z_{i}\), vehicle, incident, and cached content. The content pre-caching means the content placement for every vehicle and provides efficient results and location-based successful location areas depicted in Equation (5). \[C_{pre-cache}^{Vehicle}=\sum_{RSU}^{CM}\Big{(}V_{i},\ C^{cache},\ D_{i}\Big{)}\] (5)
* The machine learning module attached to the content manager \(CM\) early predicts the content placement and caching for the vehicles and enhances the working prediction and other related methodologies. In Figure 4, we explain the logical workflow of the proposed methodology in the proposed environment where \(V_{ZoR}\) join the network. All the pre-registered vehicles that join the network define and provide the relevant details required to set up the technology of the vehicular advance level. After the setup of the VANET environment, the new vehicle \(V_{New}^{RoZ}\) is detected and provides the control to add this vehicle through its dedicated \(V_{ID}\). The relevant information, such as \(V\), speed, zone, etc., is recorded and saved inside the selected zone, providing the effective zone of relevance. After the detection of the \(V_{New}^{RoZ}\) an alert message is issued to all the \(V_{ZoR(i)}\) and data are stored in the central manager \(CM_{Geocast}\) for future references and database development. The prediction is based on the Geocast communication defined in Section 2 of the chapter. After the alert, the dissemination is forwarded to the ZoR through the provided emergency messages, and the alert event is recovered by the ZoR. This means the target vehicle successfully finds the relevant \(V_{ZoR}\) then disseminates the alert event message to the target vehicles where the final content is required to be pre-cached. On the other hand, all the targeted vehicles must be detected if the new vehicle enters the zone. An \(V_{ZoR}^{Message}\) is forwarded to all the relevant vehicle content and provides effective relevancy in their alert event messages. Early prediction of the vehicles and alert message generation reduce the network latency and overhead of communication and content placement in emergency conditions. In case of emergency, messages are not found, or vehicles are not predicted early, the relevant system will issue the prediction-based accuracy and extend the relevant information with an eviction-based policy. Every \(RSU_{ZoR}\) is pre-checked and found to be the best of their system with effective knowledge discovery. The relevant prediction of the nearest zones is handled, providing effective knowledge discovery, and extending the related ZoR. Finally, the messages are disseminated through a pre-caching strategy through effective and forwarded links with relevant ZoR and early prediction for emergency conditions. Algorithm 2 explain the process. Figure 4: Logical workflow of the proposed methodology. ```
Input: Table 1, Algorithm 1, \(V_{2 aR}\), Geocast Network. Output: Target \(ZoR\), Messages Dissimination, Network Development Steps
1. \(Z_{i}\leftarrow\) Algorithm 1
2. for\(K_{i}\gets 1\) to \(K_{\textbf{s}}\)do
3. if\((Detect_{Event}\gets V_{ZaR}^{Gensor})\)
4. \(R_{Event}\gets V_{ZaR}*\sum M_{ZaR}\)
5. Disseminate AEM to \((\sum_{V}ZoR)\)
6. else
7. Select\((Z_{i}\leftarrow\{Z_  
sured value to finalize the position-trained model. The steps are registered after training the selected CNN-based ML model. Now it's time to test the resultant model with the testing dataset. The predictions are captured with accuracy, average delay, cache hit ratio, and network throughput. The results are obtained from the predicted values and dataset range. Algorithm 3 is a machine learning algorithm using the CNN model.