## 2 Related Work
In contrast, in our work, UAVs are used as assistance to MEC nodes to address the load problem. To reduce the ground nodes' energy consumption, the authors of [30] investigated collaborative multi-task computation and cache offloading for UAV-aided MEC networks, taking into account the time-sensitive tasks' Quality of Experience. To handle the multiple problem variants, a block coordinate descent-based solution is proposed. Three manageable subproblems were identified and solved iteratively, including trajectory optimization, UAV resource allocation (including computation capability and bandwidth), and offloading decisions that take into account task type and ratio. Multiple experiments were conducted in a simulated environment, and the authors reported that the results provided insight into the effectiveness of collaborative offloading as it was able to manage two jobs (computing and caching) while reducing the overall GN energy consumption and meeting the QoE requirements of various task types. Meanwhile, [PERSON] et al. [31] proposed a cooperative task offloading and resource allocation algorithm (CTROAA) that uses Lyapunov optimization to minimize energy consumption while taking system performance into account. The authors considered hybrid energy sources for multi-clouds with UAV assistance. Three critical subproblems pertaining to task offloading control, local computing control, and charging control and cloud computing have been considered. The first two problems were mathematically formulated as convex optimization problems, whereas the third problem was mathematically formulated as a combinatorial optimization problem and was solved using Simulated Annealing (SA). To demonstrate the efficacy of the algorithm, a mathematical analysis was conducted where authors demonstrated that CTORAA can achieve the arbitrarily defined profit-stability trade-off. In addition, simulation results were conductedto demonstrate that the algorithm could adapt to varying task arrival rates, assure the stability of the queue, and outperform two benchmark approaches, \"Fixed\" and \"Random\" in finding the optimal solution to minimize energy consumption. Elsewhere, the task-offloading problem for a multi-Internet of vehicles environment has been added in [32; 33]. Specifically, [PERSON] et al. [32] modeled task offloading, security, and resource allocation as a multi-objective problem for UAV-assisted vehicular ad hoc networks, where minimizing the task delay is the main goal. Afterward, a relax-and-rounding and Lagrangian-based algorithm is proposed to solve this problem in an effective manner. Whereas, in [33], an integrated problem of task offloading, RSUs' cooperation, and tasks' division is proposed for MEC-based vehicular networks, where minimizing the task delay and increasing the performance of the service are the main goal. Additionally, an efficient routing mechanism is introduced to boost service reliability and decrease the rate of session failures. Moreover, a model-based deep neural network is developed to solve this problem and derive the optimum solution. Furthermore, recent task offloading and resource allocation models have been proposed for MEC networks [34; 35]. Specifically, [PERSON] et al. [34] proposed a multi-tiered edge-based resource allocation optimization framework for the heterogeneous execution of tasks. This framework can facilitate the different operations of offloading over diverse IoT environments. Moreover, an optimization strategy is proposed with the goal of reducing energy consumption while promoting processing computations, task execution time, and network bandwidth. Whereas in [35], a resource allocation has been investigated for multi-UAV-aided MEC networks, in which a mixed-integer programming problem is formulated with the goal of decreasing the overall system cost in terms of latency and energy. Moreover, an efficient algorithm based on deep reinforcement learning is proposed that joined UAV movement control, MU power control, and MU association to solve this problem and derive the solution.