## 6 Performance Evaluation  
For this segment, we analyzed the efficiency of our suggested DETO approach in a UAV-enabled MEC network by conducting simulations. A suburban area was considered, to simulate the deployment of UAV-MEC servers for 300 MDs distributed within three distinct hotspots. The K-means clustering technique was used to determine the initial center position for each UAV-MEC server, based on the user distribution provided and the number of deployed UAV-MEC servers. The initial values of the major radius, the minor radius, and the rotation angle for the ellipsoidal movement of each UAV were randomly chosen within the ranges [60, 200] m and [0\({}^{\circ}\), 360\({}^{\circ}\)], respectively. The value of \(\rho\) was set to 0.5, meaning that both transmission latency and transmission energy consumption were equally important. Table 2 summarizes the rest of the simulation parameters utilized in our experiments, with default values denoted in bold.  
The Geolife mobility dataset [40] was used to determine the MDs' locations. The dataset contains GPS traces of 182 users in Beijing, China, which were collected during a five-year period by Microsoft Research Asia from April 2007 to August 2012. Our simulation covered a crowded region of \(1000\times 1000\) m\({}^{2}\) area in the city. The MD locations were then chosen from the users' GPS points within the specified area by considering the time frame 12 p.m. to 1 p.m. during the year 2008.  
For the DETO framework's neural network structures, we used two hidden layers, each with 64 neurons. These hidden layers had ReLU activation functions, whereas the output layer had a linear activation function. Table 3 represents the DETO framework's main hyperparameter representation, which was used to train the model.  
The experiments were performed using a system with an Intel Xeon E3-1245 v5 processor running at 3.50 GHz, an NVIDIA GeForce GTX 1050 Ti graphics card, and 16 GB of RAM. Implementation of the DETO framework was carried out using Python 3.8.0 and a GPU-enabled version of TensorFlow 2.9.2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Size of the area & - & \(1000\times 1000\) & m\({}^{2}\) \\ Number of MDs & \(M\) & \(\{150,200,250,\mathbf{300},350\}\) & - \\ Number of UAVs & \(N\) & \(\{1,2,3,4\}\) & - \\ Task offload request rate & \(\lambda\) & \([0.1,1]\) & task/s \\ Task size & \(1\) & \([1,10]\) & MB \\ Height of UAV & \(h\) & \(100\)[15,50] & m \\ Range of major radius & \(R_{x}\) & \([60,200]\)[43,51] & m \\ Range of minor radius & \(R_{y}\) & \([60,200]\) & m \\ Range of rotation angle & \(\theta\) & \([0,360]\) & \({}^{\circ}\) \\ Path loss parameter & \(a\) & \(9.61\) & - \\ Path loss parameter & \(b\) & \(0.16\) & - \\ Path loss parameter & \(\eta_{\text{LoS}}\) & \(1\) & - \\ Path loss parameter & \(\eta_{\text{NLoS}}\) & \(20\) & - \\ Channel bandwidth & \(B\) & \(20\) & MHz \\ Transmit power of MD & \(P_{t}\) & \(20\) & dBm \\ Noise power & \(\sigma^{2}\) & \(-100\) & dBm \\ The carrier frequency & \(f\) & \(2\) & GHz \\ The speed of light & \(c\) & \(299,792,458\) & - \\ weight & \(\rho\) & \(0.5\) & - \\ \hline \end{tabular}
\end{table}
Table 2: Experimental settings.