## 6 Simulation and Analysis
### Simulation Results and Analysis  
As shown in Figure 3, as the number of IoT devices, i.e., \(N\), increases, the delay of all schemes increases gradually. GWOORAC has the lowest delay, and the more IoT devices, the more the delay reduction. Compared with GWORandRAC, the delay is relatively close when \(N=10\). However, when \(N>10\), the delay reduction increases notably, which demonstrates that our optimal resource allocation is effective. The delay of GWOORAC is lower than that of RandORAC. Moreover, the delay reduction increases notably when \(N>15\), which proves the effectiveness of the GWO algorithm. Compared with GWOORANC, there is little difference in the delay when \(N\leq 25\). By contrast, when \(N>25\), the difference between the delay of GWOORAC and that of GWOORANC increases gradually, which indicates that the inter-satellite cooperation is of great significance in reducing the delay. The computing resources of MEC servers are much larger than those of IoT devices, and each task can be allocated more computing resources than the computing capacity of IoT devices when tasks are offloaded to satellites for computing; thus, the delay of OLC is much higher than that of GWOORAC. Moreover, although the delay increases as \(N\) increases, the increasing speed of GWOORAC is much smaller than that of OLC. Figure 4 shows that GWOORAC has the lowest delay. As the number of satellites, i.e., \(M\) increases, the delay of GWOORAC, GWORandRAC, and RandORAC decreases gradually, because more neighboring satellites bring more computing resources, and more computing resources are available for each offloaded task. Neither GWOORANC nor OLC takes into account neighboring satellites, so their delay almost does not change with the variation of \(M\). Due to the optimal resource allocation, the delay of GWOORAC is much \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(N\) & 50 & \(M\) & 5 \\ \(c_{i}\) & 1000 cycles/bit & \(a_{i}\) & [0.5, 5] Mbits \\ \(f_{i,0}\) & [1, 2] Gcycles/s & \(p_{i}\) & 2 W \\ \(g_{i}\) & 43.3 dBi & \(B_{i,1}\) & 100 MHz \\ \(B_{1,j}\) & 100 MHz & \(P\) & 50 W \\ \(F_{1}\) & 12 Gcycles/s & \(F_{j}\) & 12 Gcycles/s \\ \(D_{\text{TA}}\) & 2.2 m & \(N_{0,i}\) & \(-203\) dBm/Hz \\ \(N_{0,i}\) & \(-203\) dBm/Hz & \(R_{\text{e}}\) & 6371 km \\ \(\theta^{\text{U}}_{\text{min}}\) & 16\({}^{\circ}\) & \(lat\) & 60\({}^{\circ}\) \\ \(h_{\text{s}}^{\text{-}}\) & 780 km & \(c\) & \(3\times 10^{8}\) m/s \\ \(f\) & 30 GHz & \(K\) & 7 \\ \(I\) & 6 & \(J\) & 11 \\ \(N^{\text{W}}\) & 20 & \(t^{\text{max}}\) & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 3: Delay vs. number of IoT devices. lower than that of GWORandRAC. The delay of GWOORAC is much lower than that of RandORAC, which indicates that the GWO algorithm is appropriate to solve the problem. When \(M\geq 2\), the delay of GWOORAC is lower than that of GWOORANC, and the gap gets larger, which demonstrates that the inter-satellite cooperation reduces the delay. Compared with OLC, the delay of GWOORAC is much lower, and the delay reduction becomes larger as \(M\) increases, because a large number of computing resources can be allocated to each offloaded task, far more than the computing capacity of IoT devices. As Figure 5 shows, the delay of all schemes except OLC shows downward trends as the computing resources of each satellite, i.e., \(F_{1}\) and \(F_{j}\), increase. The reason is that the more computing resources a satellite has, the more computing resources are allocated to offloaded tasks, which reduces the delay of computing tasks. OLC does not consider computation offloading, so its delay is not affected by \(F_{1}\) and \(F_{j}\). Due to reasonable resource allocation, the delay of GWOORAC is much lower than that of GWORandRAC. The delay of GWOORAC is lower than that of RandORAC, which indicates that the GWO algorithm can obtain an appropriate task offloading decision, thereby reducing the delay.