## 6 Performance Evaluation
### Performance Comparison between DETO and Other Baselines
#### 6.2.2 Effects of Different Numbers of Task Requests  
Figure 7 shows the effects of varying numbers of task requests. With the increasing number of task requests, the total task size concurrently expands. Consequently, more data bits need to be offloaded to UAV-MEC servers from the MDs. When MDs transmit a large volume of data bits to the UAV-MEC servers, it leads to higher transmission latency and increased energy consumption. Therefore, the system objective, total transmission latency, and total transmission energy consumption shown in Figure 7a, Figure 7b, and Figure 7c, respectively, demonstrate an increase consistent with the growth in task requests. In Figure 7a, it is observed that DETO always performs better than the GA and greedy approaches, obtaining a lower system objective across different task request ranges, whereas the system objective obtained by the greedy algorithm is the highest among them. For ex  
Figure 6: Effects of different numbers of MDs: (**a**) effects on the system objective; (**b**) effects on the total transmission latency; (**c**) effects on total transmission energy consumption; (**d**) effects on total throughput.  
ample, when the number of task requests is in the range of 0.6 to 1, the system objectives obtained by DETO and GAETO are 0.737 and 0.751, respectively, while the system objective for the greedy approach is 0.765.  
The displayed curves in Figure 7b,c show a steady and gradual increase as the number of task requests increase. This is a result of the growing need to offload more tasks, which requires more time and energy for transmission. For example, in Figure 7b, the transmission latency obtained by DETO increases from 231.17 s to 328.66 s when the task request changes from the [1.6-2] range to the [2-3] range. By comparison, GAETO increases from 234.49 s to 332.45 s, and the greedy approach increases from 240.19 s to 340.33 s, respectively. Similarly, in Figure 7c, transmission energy consumption increases for DETO, GAETO, and the greedy approach, ranging from 23.11 to 32.86 j, 23.44 j to 33.24 j, and 24.01 j to 34.03 j, respectively.  
Figure 7d illustrates the system's total throughput, with DETO and GA showing a slight up-and-down trend, while greedy remains unchanged. This is because the throughput is influenced by the positions of the MDs and the positions of the UAVs along the ellipsoidal paths. The throughput follows an increasing or decreasing trend when the number of MDs increases or decreases. However, in this scenario, the positions remain unchanged for a fixed number of MDs, even though the number of task requests varies. Thus, the solutions obtained by DETO and GAETO show slight fluctuations in total throughput. Notably, the DETO-based approach consistently shows higher throughput than GAETO and greedy. Moreover, the results imply that using the NN to approximate the Q-values enables the DQN-based solution to achieve better results for the considered problem.  
Figure 7: Effects of different numbers of task requests: (**a**) effects on the system objective; (**b**) effects on the total transmission latency; (**c**) effects on total transmission energy consumption; and (**d**) effects on total throughput.  
By maximizing the total reward and locally optimizing the DQN result, the agent can learn a strategy to identify the ellipsoidal trajectory's parameters that provide a lower system objective. On the other hand, GA-based solutions partially depend on random processes, which may result in early convergence if genetic diversity is lost, resulting in suboptimal solutions. Moreover, because of the large solution space, the GA produces a considerable number of infeasible solutions in each iteration, whereas greedy uses fixed parameters to solve the considered problem. Therefore, finding a good solution is hard, using GA and greedy approaches.