## 4 Simulation Results and Discussion
### Experimental Results & Discussion  
This subsection evaluates the performance result of our model in comparison with four scenarios:  
* **Local Execution:** No offloading exists. The computation tasks of all mobile devices will be processed locally \(\varphi_{i,j,0}=1\);
* **UAVs Server Execution:** The computation tasks of all mobile devices will be processed remotely at UAVs servers \(\sum_{a=1}^{M}\varphi_{i,j,a}=1\);
* **Cloud Execution:** The computation tasks of all mobile devices will be processed remotely at the cloud server \(\varphi_{i,j,M+1}=1\);
* **Computation Offloading Execution in [39]:** The computation tasks of all mobile devices will be processed at one of the available servers, including the local execution based on the model proposed in [39]. First, the consumption of energy for executing the tasks for the mentioned scenarios in relation to different users is shown in Figure 2. As shown, our proposed model achieves the best result compared to the other scenarios. Additionally, the edge and cloud execution scenarios exceed the scenario of local execution as the number of users increases. Moreover, the edge execution policy also exceeds the cloud execution with more than 140 device users. This result is due to increases in communication time when the communication channels are shared between the users as well as the higher energy consumption during the offloading process. In addition, the UAV's server resources are not enough to handle all the device users where the cloud server is more resourceful, thereby becoming better than UAV's execution for a large number of users. Similarly, Figure 3 shows the response time for executing the computation tasks for different numbers of users. This figure shows that the response time for our proposed model is close to or equal to the response time for UAVs, cloud, and model executions in [39], while being less than the local execution policy. However, as the number of mobile users grows, the performance of UAVs and cloud execution policies improves in comparison to local execution, while our model still outperforms the local policy and outperforms the model in [39]. Further, the cloud execution policy outperforms the UAV's server execution. This is traced to the communication channels shared among users being overloaded, thereby increasing the time, whereas the proposed model can be intelligently adapted to environmental change. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of UAVs & 5 \\ Number of mobile devices & 30 \\ Height of UAVs & 20 m \\ System bandwidth & 20 MHz \\ Transmission power & 0.1 Watt \\ Input data size & [0, 10] MB Unif-Dist \\ CPU cycles to accomplish task & 1000 cycles/bit \\ Computation capability of mobile device & \{0.5,0.6,\ldots,1.0\} \\ Energy consumption & \((0,20\times 10^{-11})\)J/cycle \\ UAVs server capability & [3.5, 5] Unif-Dist GHz \\ Cloud server capability & 20 GHz \\ Cloud propagation delay & 15 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Second, the consumption of energy for executing the tasks for the mentioned scenarios in relation to different data sizes is shown in Figure 4. From this plot, we can deduce that the energy consumption of our proposed model, for data sizes of less than 20 MB, is close to the energy consumption of the UAVs, cloud, and model in [39] scenarios, whereas it is less than the energy consumption of the local execution scenario. Nevertheless, with the increase in data size, the UAVs server and cloud scenarios' performance declines compared with the scenario of local execution, whereas the performance of the proposed model remains lower than that of local policies and the policy of the model in [39]. Moreover, the cloud execution policy outperforms UAV's execution policy with large data sizes (i.e., more than 45 MB).