# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 3 Materials and Methods
### Construction of Zones

Table 2: _Cont._```
Input: Parameters from Table 1 Output:\(Z_{i}\leftarrow\{Z_{1},\ Z_{2}Z_{3}Z_{4},\....,Z_{n}\}\) Steps
1.\(Z_{size}\leftarrow\) R_select(\(RAD_{length}\), \(N\), \(K\))
2.for\(i\gets 1\) to \(J\)do
3.\(Z_{Nodes}\gets Node_{total}-Interier_{nodes}\)
4.\(Z_{Nodes}\gets Node_{total}-Interier_{boundary}\) while(meet criteria) do  for\(k\gets 1\) to \(N\)do \(Z_{table}\leftarrow\{1\}\) Endfor \(d\gets 1\) to \(i\) \(Z_{i}\leftarrow\frac{1}{|Z_{table}|}\sum_{p_{max}\in P_{max}}N_{n}\) End While
5. return \(Z_{i}\leftarrow\{Z_{1},\ Z_{2},\....,\ Z_{n}\}\)
```

**Algorithm 1** Zone Selection

### Geocasting

Geocasting is one feature used in our proposed methodology for efficient location-based message dissemination or broadcasting in specific zones. Geocast depends on location-based addresses and enhances the functioning of zone-based communication. The central manager maintains geographic location information through roadside units (RSUs) to sustain Geocasting information [35]. The target areas where Geocasting is potential in this research are proclaimed as zones, i.e., (zone of reference) ZoR, and zone of forwarding (RoF). ZoR is certain in Geocast messages. If any of the messages are required to forward or disseminate in any of the \(Z_{i}\), then ZoR is first used to carry the message and needs to be disseminated within a certain ZoR [36]. ### Role of RSU and Central Manager

Roadside units (RSUs) are the key element in vehicle-to-infrastructure (V2I) communication in VANET. Any information which is exchanged between \(Z_{i}\) (_Zones_) central content manager, RSU to RSU, and RSU to Vehicles are viable through RSU. The RSU links the vehicles, enhancing the node's working with specific fixed infrastructure [37]. Accessing the Geocasting address of every node working in zones is possible through the RSU. It fixes, continuously collects, and transmits the location information for every vehicular node joining the network. RSU, in our approach, works on radio-type communication. Additional features we extend in our approach include communication for Geocasting network infrastructure. Other responsibilities are to:

* Extend the communication range for zones \(Z_{range}^{i}\) and vehicles \(V\). * Run applications safely. * Provide internet connectivity to all other OBUs using connected dominated set algorithms [37; 38]. On the other hand, the central manager or content manager node Geocasting the pre-caching content to every zonal vehicle through ZoR and ZoF schemes. The manager node uses machine learning to pre-cache the selected zone and vehicle in every zone. The main work of the content manager is as follows. * Initially, it set the zones \(Z_{i}\) and location-based zonal boundaries placement \(Z_{boundary}^{L}\) and kept the record in a specified address table. * Central Manager collects information from the different vehicles about the road conditions. The continuous information provides adequate knowledge and discovery to convey the latest broadcasting network and settings. The proposed technique is effectively overwhelming and intends the VANET message forwarding. * It periodically checks all network resources through RSU from zones and provides content, message requests, and responses according to their use. Equation (3) elaborates it as. \[R_{RSU}^{Z_{i}}=Check\Big{(}RSU_{Zone}^{Message}\Big{)} \tag{3}\]
* After getting the emergency messages \(M_{enc}\) from the vehicles \(V\), the central manager \(CM\) is equipped with the machine learning model, which trains their aspects based on the emergency messages from the RSUs and vehicles. The training period for the machine learning model is operative and provides the knowledge to discover the proper and effective mechanism to enhance the collective management and portability analysis. * The machine learning \(ML\) module continuously handles the training in this approach, and content is processed based on the parameters passed. The prediction accuracy and traffic congestion control are effectively enhanced when machine-learning-based prediction.