## 6 Simulation Results
### Sensitivity Analysis  
It is necessary to conduct sensitivity analyses on the number of IoT devices and the number of UAVs, because the quantities of IoT devices and UAVs are very likely to vary in different real-world scenarios. In this way, the robustness of the model can be evaluated.  
Figure 10: HV curves of MOEA/D-MFAR, MOEA/D, NSGA-II, and MOPSO in eight scenarios. (**a**) Scenario 1. (**b**) Scenario 2. (**c**) Scenario 3. (**d**) Scenario 4. (**e**) Scenario 5. (**f**) Scenario 6. (**g**) Scenario 7. (**h**) Scenario 8.  
For the sensitivity analysis of the number of IoT devices, we constructed two scenarios, namely scenario A and scenario B. In these two scenarios, the numbers of IoT device types are 2 and 3, respectively, the population sizes are 100 and 120, respectively, and the number of algorithm iterations is 50 for both. Moreover, the distribution of ground devices is sparse, and other parameter settings are the same as those in Table 4. Similarly, we used the ADSR indicator for evaluation to observe the fairness of resource allocation. As the number of ground IoT devices increases, the ADSR indicators in scenario A and scenario B are presented in Table 7. It can be observed that as the number of devices increases, the ADSR shows a fluctuating and slowly rising trend. In scenario A, when the number of each type of ground IoT device reaches 1000, the ADSR is 2.28121 kb/s (bolded in the table), which is 2.08684 kb/s (bolded in the table) in scenario B. In the case where the number of each terminal is 1000, the ADSR is still within an acceptable range, which to some extent indicates that resources can still be allocated fairly, proving that the algorithm is also effective in the case of a large number of ground IoT devices.  
Furthermore, for the sensitivity analysis of the number of UAVs, we set up scenario C and scenario D. In scenario C and scenario D, the types of IoT devices are 2 and 3, respectively, and the number of each type of device is 30. The devices are sparsely distributed, and other parameters are the same as those in Table 4. Similarly, we observed the changes in ASDR under different settings of the number of drones. The changes in ASDR in scenario C and scenario D are shown in Table 8. It can be found that whether in scenario C or scenario D, as the number of drones increases, the ASDR shows a fluctuating and slowly rising trend. This is partly because the resource allocation of UAVs involves discrete resource variables, that is, the association between IoT devices and UAVs, which will increase the difficulty of fair resource allocation. When the number of drones reaches 25, the ASDR in scenario C and scenario D reaches 2.30610 kb/s and 9.56433 kb/s, respectively (bolded in the table), which are also relatively small. Therefore, the algorithm maintains a certain level of effectiveness even in scenarios with a large number of UAVs.  
Hence, it is observable that an increase in the number of IoT devices or UAVs leads to a heightened complexity in the resolution of the algorithm. This is accompanied by a trend of fluctuation and a slight yet acceptable increment in the ADSR. Therefore, the maximum number of devices and UAVs that the algorithm can handle can be limited based on the minimum requirements for the ADSR.