## 5 Simulation Results
### Simulation Results  
Figure 5 illustrates the comparison of the task processing delay with respect to the local computation resources under different algorithms. In the early stages, we find that the task processing delay of the vehicle local computing algorithm is much higher than other algorithms. This is because the computation ability of most vehicles is limited, and thus multiple tasks cannot be fully processed within the allowed latency. Moreover, with the increase of the local computation resources, the task processing delay of all algorithms decreases accordingly. When the vehicle local computing resources are larger than some values (referred to as break points), the task processing delay will decline sharply, which represents that adding a small amount of local computing resources in the initial phase can effectively reduce the task processing delay. Furthermore, we note that the UAV-assisted strategy expedites the uploading progress in the simulated scenario, which verifies the validity to apply UAVs as MEC servers, allowed them to be regarded as suitable candidates to improve VANETs connectivity. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Vehicle transmission power & 1 Watt \\ UAV transmission power & 5 Watt \\ Number of UAVs & 5 \\ Number of vehicles & [0, 60] \\ UAV computing resources & 20 GHz \\ Vehicle computing resources & 1 GHz \\ Task size & 10 MB \\ Task interval & 30 s \\ Task maximum allowed latency & 10 s \\ Simulation times & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 5: Comparison of the task processing delay with respect to the local computation resources under different algorithms. Figure 6 shows the comparison of the successful task processing ratio of the local computation resources under different algorithms. The simulation results indicate that compared with other algorithms, our proposed LBTO algorithm can offload tasks with minimal local computing resources. This is because the LBTO algorithm can take full advantage of the computation resources of all MEC servers in the entire VANETs. As we optimize the task offload ratio when the local computation resources can satisfy the basic processing requirements of tasks, the successful task processing ratio is counted as 100%. Therefore, increasing the local computation resources can also improve the successful task processing ratio. In addition, as the channel between the vehicle and the UAV has a better quality of service than the channel between the vehicle and the RSU, we can find that using UAVs for MEC can offload tasks more quickly. Therefore, the UAV-assisted VANETs architecture can provide vehicles with lower latency and more reliable computing services. Figure 7 shows the comparison of the task processing delay with respect to the vehicle speed under different algorithms. We can observe that our proposed LBTO algorithm greatly outperforms the other algorithms with different vehicle speeds, which verifies the effectiveness of our proposed algorithm. On the other hand, we can find that the task processing delay of the vehicle's local computing algorithm cannot change with the change of the vehicle speed. This is because in vehicle's local computing algorithm, tasks are not offloaded to the MEC server on UAV for processing. By contrast, with the increment of the vehicle speed, the task processing delay of the LBTO algorithm declines greatly. In addition, it can be observed that with the increase of vehicle speed, the decrease trend of task processing delay gradually slows down. Our proposed LBTO algorithm can reach the critical point (when the vehicle speed is larger than some values, the task processing delay will decline relaxedly) faster, which shows that the proposed LBTO algorithm has good applicability in both low-speed and high-speed scenarios. Therefore, these results tell us that the task processing delay can be reduced by increasing the vehicle speed appropriately.