## 2 Network Model  
Figure 1 shows the network model under Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) entities. The following are the entities used to design the VANET infrastructure [69; 70; 71; 72]. * Vehicle Unit: VANETs are responsible for communication with nearby vehicles along the highway sector or with the RSUs at the edges/corners in simple terms for transport purposes. VANETs are also responsible for facilitating transport. We used elliptical curve encryption as an encryption algorithm in this paper to generate cryptographic credential information and store it in the vehicle. The Global Positioning System (GPS) is responsible for locating the vehicle inside. In a road segment, RSUs can determine the total number of vehicles using a GPS unit [40; 41; 42; 43; 73; 74; 75; 76; 77];
* Roadside units (RSUs): The RSU is an access point on the roads, where details of vehicle units can be found along the roads. The vehicle units' encrypted street segments are regarded as the relay for the Transport Message Channel. An IoT-BA is connected to RSU by a faster means of communication. The RSU operates the cryptographic credentials to decode vehicle information and the DNN-IoT-BA algorithm and stores them;
* Traffic Management Center (_TMC_): The IoT-BA calculates the traffic density using road segments. In order to obtain road traffic information, the directional connection of TMC to RSUs and other IoT-BAs are used. In order to avoid congestion in road segments, the IoT-BA transfers the collected information to the DNN. The differential between TMC and IoT-BA is that the former is a fixed segment and the latter is a network-wide mobile segment. The proposed method was tested in an urban scenario that was designed as grids and intersections of roads. The path between any two crossings was considered to connect two or more street segments, such as the segment \(s(i,j)\) and intersection \(I(i)\)\(s(i,j)\), providing a collection of different characteristics, which include road widths and lengths, vehicle traffic density, and the number of roads between two intersections [78; 79; 80]. The unit has a unique identification number (ID), consisting of the location, direction, and speed of the vehicle. Vehicles automatically accessed the RSU to understand total crossroads, traffic segments, and congestion levels. This study used IoT-BA to provide more precise vehicle information than existing vehicle communication models. The DNN made  
Figure 1: Vehicular Adhoc Networks (VANETs) model. appropriate decisions by forming the graph \(G=<V\),\(E>\) with \(V\) in the proposed method, as the intersections between the source, the destination vehicle, and \(E\) are considered the road segments connected to intersections in \(V\). The below definitions can be considered as parameters that append themselves for optimizing the DNN via BA:  
* Detection range is defined as the communication region between the receiver sensitivity threshold of any two vehicles and the SINR, which are required for payload;
* Data Exchange range is defined as the communication region in which the data transmission takes place;
* Time before handover is defined as the communication region where the OBU prepares for handover;
* Time to handover is defined as the communication region where the actual handover takes place. The minimum received power is defined as the minimum power a car requires to physically receive the signal from RSUs, which is defined below  
\[\text{Min}\ (P_{R})=10^{0.1*\text{sat}} \tag{1}\]  
where _sat_ is defined as the minimum signal attenuation. The detection range is defined as the minimum reception power needed to establish communication based on wavelength, transmitter power, threshold, and path loss coefficient  
\[\text{Detection Range}=(\lambda^{2}\text{max}(p))/(16\pi^{2}P_{R}))^{1/\alpha} \tag{2}\]  
where:  
\(\lambda\) is the wavelength;  
\(\text{max}(p)\) is the maximum level of transmission power;  
\(\alpha\) is defined as the minimum level of a path loss coefficient.