## 2 Related Work  
In the last decade, the computing power of Unmanned Aerial Vehicles (UAVs) has improved by several orders of magnitude. UAVs have emerged as a promising technology for supporting a wide range of human activities, including target tracking, water quality inspection, disaster relief, and power patrol inspection to surveillance due to their inherent mobility and increasing computing power. As a consequence, there is significant interest in embedding UAVs into edge computing systems to provide value-added edge computing services. The recent proliferation of IoT devices, in conjunction with edge computing, have uncovered numerous opportunities for novel UAV-based applications. Due to their limited battery life and processing power, however, UAVs face difficulties when performing computationally intensive tasks. Multiple research proposals on UAV-assisted MECsystems have been proposed. Learning and optimization have been widely employed in the development of such systems. In addition, single-UAV edge computing systems may not be adequate for serving remote users and accommodating diverse UAV application scenarios. Therefore, solutions based on multiple UAVs have been proposed to handle this issue. The authors of [21] proposed iTOA, an intelligent task offloading approach for UA MEC networks, to help in determining which offloading tasks would alleviate the burden on the UAV computation platform and be most beneficial to it. The key features of their work are the use of the deep Monte Carlo tree search (MCTS) scheme and a splitting deep neural network (sDNN). The role of MCTS is to find the optimal offloading trajectory that maximizes some reward functions having to do with latency. More specifically, the problem has been formulated as an average service latency minimization task. In order to make room for the application of MCTS, the task offloading problem has been recast as a Markov decision process (MDP). The authors further integrated MCTS with a Long Short-Term memory (LSTM) network to better predict the quality of channels, whereas the second serves to hasten search convergence by providing prior probabilities to the MCTS being used. The simulation findings show that the proposed iTOA has 33% and 60% lower latency than the game theory and greedy search benchmark task offloading methods, respectively. Similarly, the work presented in [22] uses MDP and reinforcement learning to address the MEC task offloading problem in a UAV patrol system. To that purpose, a two-stage Stackelberg game model has been developed to simulate the system, which includes UAVs and network edge nodes. The problem has also been described as an optimization problem that utilizes a multi-agent deep deterministic policy gradient (MADDPG) scheme to find its solution. Extensive simulations were conducted in order to show the effectiveness of the suggested approach in terms of delay, utility of the UAVs, and system performance when compared to a random approach, a non-dominated sorting genetic algorithm, and a QoS priority algorithm. In another scenario, UAVs have been utilized to offer IoT devices with MEC services, particularly in scenarios such as terrestrial signal blockage and shadowing that make it difficult for IoT devices on the ground to reach edge clouds. In this regard, the authors of [23] addressed the requirement of MEC quality of service and UAV battery limited lifetime and provided a system that takes into account IoT job offloading and UAV placements. The topic is formulated as a non-convex problem that minimizes the weighted sum of service delay of IoT devices and energy consumption of UAV. To tackle the problem, the authors employed an algorithm based on successive convex approximation. The authors carried out a number of experiments to show that their collaborative UAV-EC framework outperforms baseline methods that rely only on UAVs or ECs for MEC in IoT. The authors of [24] explore scenarios in which complex missions with interdependent tasks must be carried out using multiple UAVs.