## 5 Simulation Results
### Simulation Results
Figure 6: Comparison of the successful task processing ratio with respect to the local computation resources under different algorithms. Figure 8 demonstrates the comparison of the task offloading efficiency with respect to the vehicle speed under different algorithms. It is observed that our proposed LBTO algorithm outperforms other algorithms in terms of the task offloading efficiency. In addition, the task offloading efficiency of all algorithms (except the vehicle local computing algorithm) increases with an increasing vehicle speed. The higher speed implies that a certain task can pass by more MEC servers in a given timeslot. The simulation results in Figure 8 are similar to those in Figure 7; we can find that with the increase of vehicle speed, the growth trend of successful task processing ratio gradually slows down. Our proposed LBTO algorithm can reach the critical point (when the vehicle speed is larger than some values, the successful task processing ratio will increase relaxedly) faster. Therefore, as the vehicle speed increases, the LBTO algorithm can make better MEC server choices. Figure 9 plots the comparison of the successful task processing ratio with respect to the UAV flight altitude under different algorithms. As can be seen from the results, with the increase of the UAV flight altitude, the successful task processing ratio increased first and then decreased. This is because the channel power gain from the vehicle to UAV increases with the UAV flight altitude in low-altitude scenarios, which can improve the transmission rate. In high-altitude scenarios, the channel power gain from the vehicle to the UAV decreases with the UAV flight altitude. The reason is that excessive UAV flight altitude can increase the path loss, which can lead to a decrease in the successful task  
Figure 8: Comparison of the successful task processing ratio with respect to the vehicle speed under different algorithms. Figure 7: Comparison of the task processing delay with respect to the vehicle speed under different algorithms. processing ratio. Therefore, increasing the UAV flight altitude appropriately can improve the transmission quality, which can increase the successful task processing ratio. Figure 10 depicts the comparison of the successful task processing ratio with respect to the number of vehicles under the number of MEC servers on UAVs. From this figure, we can observe that increasing the number of UAVs can improve the performance of UAV-assisted VANET in terms of the successful task processing ratio. However, the successful task processing ratio growth slows as the number of UAVs increases. This is because the task has a maximum allowed latency, and therefore the MEC servers on UAVs far from the starting point cannot serve the vehicle well. Therefore, we can increase the computing resources of the remote MEC servers on UAVs to provide better computing services for the vehicle. In addition, it can be seen that especially in the scenario with a small number of vehicles, the MEC-enabled UAV-assisted VANETs architecture can better provide task processing services for vehicles. Compared to the MEC-enabled RSU-assisted VANETs, our proposed network architecture has lower cost and better coverage capability, which can be regarded as the potential candidates for providing different type of solutions in the futuristic ITS. Figure 11 illustrates the comparison of the successful task processing ratio with respect to the local computation resources under different task sizes. We can observe that the  
Figure 10: Comparison of the successful task processing ratio with respect to the number of vehicles under the number of MEC servers on UAVs. Figure 9: Comparison of the successful task processing ratio with respect to the UAV flight altitude under different algorithms. successful task processing ratio increases sub-linearly with the local computation resources. This is because, with the increment of local computation resources, more resources can be exploited to process tasks.