## 4 Performance Evaluation  
The DNN-IoT-BA was simulated in a python compiler (Anaconda with Python 3). DNN simulation parameters under VANETs are presented in Table 1. The simulation was performed on a two-way road with a fixed road width in an area of 1500 m \(\times\) 1500 m. The simulation covered 300 vehicles in an urban scenario with speeds ranging from 0 kph to 50 kph. The IEEE 802.11p MAC protocol was used to estimate the packet interval and update the report on secured packet transmission. It also helped maintain the retransmission of packets that fail during the broadcast. The Krauss model was considered as the mobility model in the current study, which is a spatially continuous, car-following model, where the distance between the vehicles can be estimated by the VU itself. It further allowed the safe travel of VUs with proper velocity. Figure 3: Infrastructure Unit Workflow. The validation of the DNN-IoT-BA proposed was conducted against the existing models of deep-learning: DNN and Artificial Neural Network (ANN) [65]. The DNN and ANN were trained without the predictions from BA and were trained directly with the proposed module without the inputs from BA. Validation was performed by different performance metrics, such as transport type, speed of the vehicle, and network density, to assess the average latency and cumulative distribution function (CDF). The network connectivity quality of the existing DNN and ANN models is illustrated in Figure 4. The transmission range varied from 200 m to 500 m and the vehicle's arrival rate was fixed from 30 kph to 50 kph. The simulation results show that the probability of connection expired with an increasing distance metric and the connection to a vehicle was lost when distance from the RSU was increased by 400 m.  
Placing RSU on all 350 m, however, helped the VANETs build long-term connections with vehicles. Nevertheless, the connection probability still presented a challenge when connecting vehicles within the 300 m range (as illustrated in Figure 5), with increasing network traffic (from 100 to 300 vehicles). The CDF results from the DNN-BA, DNN, and ANN, testing the connection probability between 100 and 300 vehicles, are shown in Figure 5.  
In this respect, the density of the network was high for 300 vehicles, mid-range for 200, and low for 100. The results show that a more distant connectivity to vehicles was established under the proposed method than using low-density models of DNN and ANN. Network degradation was shown when the density of the network increased. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Total vehicle units & 100–300 \\ Channel carrier frequency & 5.9 GHz \\ Packet length & Uniform \\ Maximum transmission & 20 mW \\ Simulation area & 1500 m \(\times\) 1500 m \\ Vehicle velocity & 20–50 kph \\ Bit rate & 18 Mbps \\ Signal attenuation threshold & \(-90\) dBm \\ Path loss coefficient & 2 \\ Transmission range & 500 m \\ MAC protocol & 802.11 P \\ Traffic type & CBR \\ Beacon interval & 0.5 s \\ Data rate of MAC & 6 Mb \\ Mobility model & Krauss model \\ CBR rate & 4 packets/sec \\ Simulation time & 1000 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters. Figure 4: _Cont._  
The results of the average data latency between the vehicles are shown in Figure 6, depending on their velocity. The simulation results show that data transmission latency increased alongside increases in speed. On the other hand, the latency continued to increase with increasing network density. The combined surges in speed and vehicle density contributed to the maximum latency. Such mobility affects the transmission of data because the link between vehicles is not established. This affects the delivery rate directly because the average latency is indirectly proportional to the ratio for package delivery (Figure 7). The simulation results, on the other hand, show that the link was well established, with minimal vehicle speed and density, and the average delay was, therefore, significantly reduced.