# Satellite Edge Computing for the Internet of Things in Aerospace
## 5 Performance Evaluation  
In this section, we show illustrative results to demonstrate the performance of our proposed satellite edge computing system. We will compare the performance of satellite edge computing systems with traditional satellite constellations through two indicators: total time consumption and total energy consumption. The total time and total energy consumption to complete all the tasks are defined by Equations (15) and (16), respectively. According to the following settings, the total time and total energy consumption of space edge computing and traditional satellite constellations are compared. The upstream rate is 10 Mbps, and the downstream rate is 100 Mbps. The transmitting power \(p_{s}\) = 0.12 W. The size of end-user data for each task is a random number within 400 M. Additionally, the effective switched capacitance \(\kappa\) = 10\({}^{-28}\), the CPU resource on satellite is 1 GHz, and the processing capacity is 1000 cycles/bit. These parameters are obtained by referring to [23; 31]. The orbit of the Iridium satellite constellation is chosen as the simulated constellation orbit. The specific parameters are shown in Table 1. The experimental constellation consisting of 66 satellites is simulated by automatically building the Walker constellation in STK. Through STK, we obtain the satellite coordinates of the simulated satellite constellation throughout January 1, 2000. The sampling interval is 10 s.  
The simulation of the specific work of the satellite is implemented in MATLAB. The latitude and longitude of the terrestrial cloud computing platform are fixed, and the latitude and longitude of the terrestrial user terminal are randomly generated. The tasks generated by each terminal follows a Poisson process. The average generation rate is \(\lambda\). It is worth noting that most of the results of simulation studies in this section are based on an average over a few Monte Carlo simulations for various system parameters. The service flow of traditional satellite is as follows:  
Step 1: The terrestrial user terminal generates tasks. Step 2: The terminal sends the task to the satellite. Step 3: The satellite forwards the mission to the terrestrial station. Step 4: The terrestrial station sends the results back to the satellite after processing. Step 5: The satellite sends the result back to the terrestrial terminal. The processing flow of the space edge computing system is as follows:  
Step 1 and Step 2 are the same as the traditional satellite processing steps. Step 3: iSat will choose whether to offload the task to the terrestrial cloud computing platform. Step 4: If the task is executed on iSat, the results will be sent back to the terminal. If the task is offloaded to the terrestrial cloud computing platform, the result is sent back to the ground terminal via satellite after the task is executed. Figure 6 evaluates the total time and energy for all tasks in terms of the number of terrestrial terminals, respectively. With the increase in terminals, the total time of tasks has increased almost linearly. The time spent in the traditional mode is much longer than that of the space edge computing. Additionally, we compared the performance of the number of satellites on the total time spent. In order to ensure basic ground coverage, we have directly reduced several orbital planes. As the number of \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of Planes & 6 \\ Number of Satellite per Planes & 11 \\ Semimajor Axis & 7159.14 km \\ Inclination & 86.4\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters of the satellite constellation. satellites decreases, time-consuming increases rapidly. Especially when the number of satellite orbital plane is small, the time consumption will increase significantly. Although increasing the number of satellite planes helps to reduce the total task time, it will lead to an increase in the cost.