## 5 Performance Evaluation
### Results and Discussion  
In this subsection, the performance of our proposed \(ETE_{\text{FN}}\) is analyzed and compared with STF, LTF and task processing with FCFS. The comparative results are obtained to calculate the task processing time and number of tasks performed along with their percentages for all three varieties of tasks individually. Fog-computing nodes are preferred over cloud-computing servers due to the close proximity of vehicles. Fog nodes provide better downloading data rates compared to cloud servers that are remotely placed and require Internet bandwidth to access. The tasks offloaded by vehicles are preferred to be executed by a fog node compared to the remotely \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline RSU coverage area & 2000 m \\ \hline Number of priority tasks & 3 \\ \hline Number of offloaded each priority task & 30 \\ \hline Vehicle speed (m/s) & 20\(\sim\)40 \\ \hline Data rate for vehicle to fog node & 8 Mbps \\ \hline Data rate for vehicle to cloud & 2 Mbps \\ \hline Emergency tasks size (kB) & 5\(\sim\)20 \\ \hline Traffic management tasks size (kB) & 12\(\sim\)30 \\ \hline Infotainment tasks size (kB) & 25\(\sim\)60 \\ \hline Fog node processing capacity (kB) & 500 \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. placed cloud servers. Figure 2 shows a comparative analysis of task execution time between cloud servers and fog nodes. It is evident from the results that fog nodes compute the same tasks with considerably less time compared to the cloud servers. Figures 3 and 4 show a comparative analysis of the number and percentages of tasks performed by fog-computing nodes for a fixed number of offloaded tasks in different processing cycles, respectively. There are 90 offloaded tasks in each processing cycle, 30 each for high, medium and low-priority tasks. Both figures are comprised of three subfigures for three types of offloaded tasks. Figure 3 shows that for high-priority emergency tasks, \(ETE_{FN}\) allows fog computing nodes to compute a maximum number of tasks compared to the other three schemes. For moderate- and low-priority tasks, \(ETE_{FN}\) forwards almost all the tasks to the cloud for processing as it almost reaches the processing capacity of fog nodes. However, LTF has the lowest number of high-priority tasks among all schemes. This is due to the fact that the size of most of the low-priority tasks is larger compared to the other two priority types. On the other hand, STF also processes the majority of the high-priority tasks due to their smaller task sizes but less than the proposed scheme. However, FCFS allows the fog node to execute all three types of tasks according to their random arrival. The results shown in Figure 4 represent the task processing percentages of these schemes for different priorities of tasks individually. The results show that \(ETE_{FN}\) executes about 84% of the high-priority tasks in all execution cycles. However, it only execute 3.5% of medium-priority tasks and none of the low-priority tasks is executed, whereas STF executes 70% of the high-priority tasks that is followed by FCFS and LTF. FCFS allows the fog node to process all three types of tasks, and LTF is top in processing most of the low-priority tasks, and executing 38% of these tasks. Figure 2: Task execution time in fog and cloud servers for varying number of RSUs. Figures 5 and 6 show comparative performance analysis of \(ETE_{FN}\) with the other three schemes for a varying number of offloaded tasks and percentage of tasks performed by fog node in different processing times, respectively. All three different priorities of tasks are shown in subfigures separately. The number of tasks in each figure is incremented by five, and the processed tasks are accumulated by adding the current tasks to previously processed tasks. Figure 4: Percentage of tasks performed by fog nodes in different processing cycles. Figure 3: Number of tasks performed by fog nodes in different processing cycles.