## 6 Performance Evaluation
### Performance Comparison between DETO and Other Baselines
#### 6.2.1 Effects of Different Numbers of MDs  
Figure 6 shows the system objective, total transmission latency, total transmission energy consumption, and total throughput of the three methods. With an increase in the number of MDs, all four figures show an increasing trend. This occurs because when the MDs increase in number, the tasks that need to be offloaded also increase, which results in higher system objectives. Additionally, as the number of MDs increases, there is a change in their distribution. This can cause the distance between MDs and the UAV-MEC servers to increase, which leads to longer latency and more energy consumption during transmissions, further contributing to the rise in the system objective.  
From Figure 6a, we see that the system objective increases gradually when the MDs increase in number from 150 to 200, from 200 to 250, from 250 to 300, and from 300 to 350, respectively, and that DETO performs better than the other two algorithms, achieving a lower system objective. For example, the value of the system objective with DETO is 0.82 when the number of MDs is 300. On the other hand, the objective values from GAETO and the greedy approach are 0.83 and 0.84, respectively. Moreover, the gap between the system objectives increases with the increase in MDs, and the gap is especially wider when MDs number 350 because more tasks need to be offloaded, and GAETO and greedy provide suboptimal solutions.  
From Figure 6b,c, it can be observed that both total transmission latency and total transmission energy consumption for all three algorithms experience a relatively sharp increase as the number of MDs increases from 150 to 200 and from 200 to 250. This increase occurs due to the increased task requests associated with the growing number of MDs, resulting in longer transmission latency and greater transmission energy consumption. However, the rise is slightly slower when MDs increase from 250 to 300. This is because when the number of MDs is 300, the majority of the users have a task request rate in the 0.1 to 0.5 range. Having lower task requests results in lower transmission latency and transmission energy consumption. It again follows a sharp increase with higher task requests when MDs increase from 300 to 350. DETO's performance is always better than GAETO and the greedy algorithm. For example, when the number of MDs is 300, total transmission latency with DETO is 63.76 s, which is 1.37% and 2.99% lower than GAETO and the greedy algorithms, respectively.  
Figure 6d illustrates the effect of the different numbers of MDs on the total throughput of the system. With an increase in the number of MDs, the total throughput experiences a sharp rise. This occurs because the channel bandwidth for each MD is assumed to be fixed. Therefore, when the number of MDs increases, the system's total throughput alsoincreases. In terms of DETO, the total throughput increases by 31.39%, 24.80%, 20.27%, and 17.09% when the MDs increase in number from 150 to 200, from 200 to 250, from 250 to 300, and from 300 to 350, respectively. Moreover, the performance of DETO is 4.33%, 3.92%, 3.87%, 3.51%, and 4.33% higher than the greedy approach for all numbers of MDs.