## 4 Simulation Results and Analysis
### Impact of the Number of Vehicle Terminals on the System  
Figures 2 and 3, respectively, show the influence of the number of vehicle terminals on the total delay and energy consumption of the system. As can be seen from Figure 2, the total delay of every scheme increases with the increase in the number of vehicle terminals, but the total delay of IBS is always the smallest. As the number of terminals increases, the computing resources required to process tasks also increase. When the number of vehicle-mounted terminals exceeds 20, the delay of MEC computing scheme increases rapidly and exceeds that of the Local computing scheme, as there is no reasonably designed task offloading sequence in the process of task offloading in the MEC computing scheme, and some waiting links occur, so the delay will become higher and higher. As we can be seen from the figure, when the number of on-board terminals is 30, the delay of the proposed scheme is reduced by 51.08%, 55.47%, 36.87%, and 18.16%, respectively, compared with the Local algorithm, MEC algorithm, PSO algorithm, and BES algorithm.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Numerical** \\ \hline The launch rate, at which a moving vehicle uploads a task \(P_{i}^{up}\) & 5 W \\ Computing resources for moving vehicles \(f_{i}^{1}\) & 1 G cycles/s \\ Computing resources for MEC \(f_{i}^{ance}\) & 4 G cycles/s \\ Computing resources provided by the cloud server \(f_{i}^{cloud}\) & 10 G cycles/s \\ Device power of the MEC server \(P_{mec}\) & 30 w \\ Device power of the cloud server \(P_{cloud}\) & 70 w \\ Channel bandwidth between car and MEC \(B_{1}\) & 8 \(\times\) 10\({}^{4}\) Hz \\ Channel bandwidth between vehicle and BS \(B_{2}\) & 6 \(\times\) 10\({}^{4}\) Hz \\ Calculate the average transmission wait delay for a task \(T_{cloud}\) & 10 ms\(\sim\)15 ms \\ The size of input data used for calculation \(d_{i}\) & 0.5–5 MB \\ The computing resources required to complete task \(c_{i}\) & 0.2–8 G cycles/s \\ Weight factor: \(\alpha\) & 0.5 \\ Maximum delay constraint \(T_{i}^{\max}\) & 0.5 s\(\sim\)1.5 s \\ Device power power of moving vehicle \(P_{i}\) & 4 w\(\sim\)8 w \\ White noise \(N_{0}\) & \(-\)100 dBm \\ The power of the mobile vehicle to upload the task \(P_{i}^{up}\) & 1.5–2 w \\ The output data volume factor \(\lambda\) & 0.15 \\ The maximum number of iterations \(Max\_gen\) & 100 \\ The parameter controlling the spiral trajectory \(a\) & 10 \\ The parameter controlling the spiral trajectory \(R\) & 1.5 \\ Parameter for controlling position changes \(\beta\) & 2 \\ The intensity of movement of the bald eagle towards the optimal & 1 \\ and central points \(c1,c2\) & \\ MEC server coverage radius & 50–60 m \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 3 shows that the system energy consumption also increases with the number of onboard terminals, but the energy consumption of the IBS is the smallest. The energy consumption of PSO, BES, and IBES computing schemes is lower than that of MEC, as they can select a task offloading platform according to the amount of data required by the task, while the IBES computing scheme has the lowest energy consumption due to the superiority of its algorithm and the addition of the task priority matrix. As can be seen from the figure, when the number of on-board terminals is 30, the energy consumption of the proposed scheme is reduced by 29.81%, 23.28%, 17.98%, and 9.32%, respectively, compared with the Local algorithm, MEC algorithm, PSO algorithm, and BES algorithm.