## 7 Conclusions  
In this study, we focused on a multi-UAV-assisted MEC system deployed in a festival area. The UAVs followed ellipsoidal trajectories, to provide MEC services to MDs that had latency-sensitive and computation-intensive applications, each with diverse task offloading requests. To enhance the system's efficiency, we formulated an optimization problem aimed at minimizing the weighted sum that encompassed both the total transmission latency and the total transmission energy consumption experienced by the MDs. This optimization approach involved the optimization of the key parameters that defined the UAVs' ellipsoidal trajectories: specifically, the center position, the major radius, the minor radius, and the rotation angle. These trajectory adjustments precisely dictated the UAVs' position and size and the shape of their elliptical movements. Furthermore, the problem accounted for constraints related to required throughput, transmission time, and energy consumption. We developed a DRL framework called DETO to effectively address the challenges posed by a vast state space and a high-dimensional action space. To determine the effectiveness of our proposed DETO method, we conducted comparative evaluations against two alternative approaches. These evaluations were carried out across diverse scenarios, encompassing varying numbers of MDs, distinct task request rates, and different numbers of UAVs. The results illustrated the efficiency of DETO in optimizing the ellipsoidal trajectory for each UAV, resulting in reductions in both the transmission latency and the transmission energy consumption of the MDs. Furthermore, when the number of users and task request rates increased, we observed an accompanying rise in both the transmission latency and the transmission energy consumption of the MDs. Figure 8: Effects of different numbers of UAVs: (**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. This phenomenon was attributed to the increased task requests, which naturally led to higher demands on the system. Conversely, when the number of UAVs was increased, both the transmission latency and the transmission energy consumption decreased, owing to the reduced distance between the MDs and the UAVs. Overall, the simulation results demonstrated that the DETO framework outperformed the baselines, due to its ability to learn from experiences and iteratively enhance solutions, establishing a significant advantage in various scenarios. For future work, we aim to enhance the existing framework by considering additional optimizing variables, including the height of the UAV, taking into account the dynamic mobility of MDs within the festival area. In addition to optimizing task transmission latency and energy consumption, we will extend our objectives, to include the minimization of the total task completion time (including task transmission and processing time when offloaded to the UAV) and the overall system's energy consumption. We plan to apply a more advanced RL method and to investigate the effect of varying \(\rho\) on the system's objective. Conceptualization, R.S., S.A. and S.Y. ; methodology, R.S., S.A. and S.Y. ; software, R.S. ; validation, R.S. ; formal analysis, R.S., S.A. and S.Y. ; investigation, R.S., S.A. and S.Y. ; resources, R.S., S.A. and S.Y. ; data curation, R.S., S.A. and S.Y. ; writing--original draft preparation, R.S., S.A. and S.Y. ; writing--review and editing, R.S., S.A. and S.Y. ; visualization, R.S., S.A. and S.Y. ; supervision, S.Y. ; project administration, S.Y. ; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2021 RII1A3051364. Not applicable. Data availability Statement: Data are contained within the article.