# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 5 Simulations
### Simulation Results  
Figure 3 shows the relationship between task offloading strategy and the processing capability and the prices of service. As we can find in Figure 3, with the increasing of the processing capability of the edge servers, the amount of the tasks offloaded to the edge servers increases accordingly. Moreover, the higher the price of ECP, the higher the number of tasks offloaded to the edge servers. Figure 4 demonstrates that, with the increase of the computing capability of the edge servers, the utility of the requesting vehicle will increase also. When the price of the ECP increases, the utility of the requesting vehicle will decrease. Thus, the simulation results show that the utility of the requesting vehicle increases as the pro  
Figure 3: Impact of the processing capability and service price on the performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(Q_{i}\) & 3 Mbits & \(p_{iv}\) & 0.6 & \(\varphi\) & 1 \\ \(r_{v}\) & 0.8 Mbps & \(p_{ie}\) & \((0.2,0.5)\) & \(\rho\) & 0.8 \\ \(r_{e}\) & 0.6 Mbps & \(\alpha\) & 0.002 & \(\mu\) & 1.2 \\ \(C_{\varphi}\) & [3; 5] MHz & \(\sigma\) & 1 & d & 0.6 Mbps \\ \(C_{je}\) & [2; 3] MHz & \(\omega\) & 1.9 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The simulation parameters. edge server or ECP rises, and decreases as the service price rises. Moreover, when the processing capability of the edge server increases or the price of the ECP increases, the requesting vehicles are more inclined to obtain resources from the edge servers. Then, we compare the proposed model in this paper with two other traditional edge server-based schemes. Our solution is denoted as \"VEC+ECP\", and the other two are denoted as \"VEC\" and \"VEC+ECP+Random\". The \"VEC\" solution means that the requesting vehicles only request computation service from the edge servers and the parking vehicles do not participate in the task offloading. The \"VEC+ECP+Random\" solution means that no gaming is performed and the tasks are randomly offloaded to the edge servers and the ECP. Figure 5 shows the comparison of the utilities of the three schemes for the requesting vehicles when the ECP price changes. We can find that the requesting vehicle has the highest utility in the VEC+ECP solution. When the ECP price is relatively low, both the VEC+ECP and the VEC+ECP+Random have a higher utility of the requesting vehicle than the VEC solution. Conversely, when the price is high, the performance difference between these three solutions becomes smaller and the requesting vehicles may be discouraged from obtaining computation services from the ECP due to the high price. Figure 6 shows the utility of the requesting vehicle with the increasing processing capability of the edge server in the three solutions. From Figure 6, we can find that our solution has the best performance. Compared with the VEC solution, our solution has the utility of the requesting vehicle increased by 10.99%. When the processing capability of the edge server is relatively high, the requesting vehicles prefer to obtain computation services from the edge servers. For example, when \(C_{v}\) is greater than 3.5, the utility in the VEC solution is higher than that in the VEC+ECP+Random solution. In addition, the utility of the requesting vehicle in this scheme becomes higher as the computational capability increases. This is because the VEC servers and ECPs need to compete with each other and must reduce their prices to attract more requesting vehicles. Figure 4: Impact of the processing capability and service price on the utility of requesting vehicle. Figure 7 shows the process of the GSA algorithm to find the best strategy via iterative optimization. From the figure, it can be seen that the numbers of iterations for the three solutions are 15, 23, and 36, which indicates that the proposed algorithm has better convergence and can find the best strategy quickly.