## 6 Conclusions  
For multi-user, multi-tier UAV-aided MEC systems, an integrated model of load balancing, resource allocation, and task offloading is proposed. In this model, an effective load-balancing model is designed to optimize the load among ground MEC servers by handing off users in the intersection area between GBSs to the most suitable one. In addition, UAVs are utilized as potential MEC servers to provide communication and computation resources by hovering over crowded areas where the ground-based MEC server is still overloaded. In addition, task offloading, load balancing, and resource allocation are jointly optimized via a formulation of an integer problem with the primary objective of minimizing system cost. This formulation is of the NP-hard variety, which is challenging to solve in polynomial time. For this problem, a novel form of deep reinforcement learning is presented in which the application task's requirements represent the system state and the offloading decision is used to define the action. The solution is then derived using an efficient distributed deep reinforcement-learning-based algorithm. In conclusion, experimental results demonstrate that our model converges quickly and significantly reduces system cost (i.e., about 41.9%, 44.2%, and 11%) compared to local execution, full offloading policies, and the task offloading work in [47].  
Figure 10: A comparison of total cost for different numbers of GBSs (K = 5, N = 3, K = 100).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Label** & **CPU Cycle/Byte** \\ \hline Health Monitoring & A & 500 \\ Automatic Number Plate Reading & B & 960 \\ \(\chi\)z64 CRB encode & C & 1900 \\ Traffic Management & D & 5900 \\ Augmented Reality & E & 12,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Applications complexity [48].  
In future work, the proposed model will be extended and we address the limitations of bandwidth, especially for larger data size transmissions, by introducing a new data compression layer. Moreover, protecting the applications' data during the offloading process will be addressed. Finally, a more general case of multi-users and multiple edges of the MEC system will be considered, in which the mobility between edge servers is considered.  
Conceptualization, I.A.E. and S.M.; methodology, I.A.E.; software, M.H.; validation, S.M. and I.A.E.; formal analysis, I.A.E. and S.M.; investigation, I.A.E.; resources, S.M.; data curation, M.H.; writing--original draft preparation, I.A.E.; writing--review and editing, S.M. and M.H.; visualization, I.A.E.; supervision, S.M.; project administration, I.A.E.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R196), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors would like to acknowledge the Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R196), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.