## 3 UAV Computing
By examining the instability of energy arrival, stochastic computation workloads offered by wireless users, and a time-varying channel state, a Markov Decision Process (MDP) approach was developed to minimize the latency, bandwidth, and energy consumption in UAV-assisted MEC [92]. Furthermore, the authors of [93] devised an optimization problem based on MDP to solve UAV path optimization in UAV-mounted MEC networks. The goal of the technique was to improve overall system QoS [94; 95; 96; 97; 95; 97], optimize long-term system rewards, and fulfill the QoS constraint, which included mobile users' computing duties. The use of Reinforcement Learning (RL) for QoS enhancement was studied in a multi-UAV MEC system to also achieve path planning and cost savings [98] with optimal locations [99; 100]. Figure 4: Cloudlets of multi-UAV computing collaboration and UAV-assisted MEC. The authors of [101] employed a semi-Markov process and DRL-based algorithms to handle the offloading choices and resource management rules of UAV-enabled MEC networks. A DL-based resource scheduling algorithm was proposed in [102] for hybrid MEC networks wherein the ground BSs, UAVs, and ground vehicles all have edge computing facilities to assist IoT devices with computing task offloading. In this work, the authors proposed an online algorithm for task offloading to minimize IoT devices' energy consumption. This is achieved through an optimization of the positions of the UAVs, the ground vehicles, user association, and resource allocation. The authors in [103] applied the AI techniques of FL and Reinforcement Learning for intelligent task offloading and computing resource allocation decisions in a UAV-NOMA MEC system. The development of next-generation communications would be aided by UAV-assisted MEC-based DL and DRL [104]. Moreover, the Deep Supervised Learning (DSL) technique reduces mobile users' computing and offloading overheads and costs in the MEC system [105]. This approach enables terrestrial users to achieve an appropriate offloading strategy that saves energy and improves processing performance. In UAV-enabled MEC networks, the authors of [106] employed RL and transfer learning algorithms to minimize latency and energy consumption. They demonstrated that combining transfer learning with RL may greatly improve system training performance when users operate dynamically. The authors of [107] applied AI approaches to UAV-NOMA-MEC networks. They presented an architecture built on FL and reinforcement learning. Furthermore, reference [108] discussed resource allocation and path design for drone-aided MEC. In [109], the authors described a flying UAV-MEC platform, in which UAVs are equipped with computer resources and provide task-offloading services to users. The fundamental goal of the proposed design was to improve UAV aerial trajectory, user association, and resource allocation. The optimization problem was solved using the trajectory control approach, aided by an RL-based strategy. This strategy outperformed the outcomes of comparable benchmark approaches. Furthermore, by evaluating the user's mobility data to discover ideal initial UAV deployment sites [110], an ML-based innovative framework was created to optimize UAV trajectories. A multiagent Q-learning-based approach was used to handle the combined problem of UAV trajectories and power control to increase the sum rate and maintain the data rate needs of mobile users. A smart offloading technique based on deep Q-learning was presented in [111] to maximize performance in terms of the delay observed by users linked to the Flying Ad hoc Networks (FANET) to allow MEC in the B5G field. The combined trajectory design in UAV computing networks is a crucial design difficulty in meeting computing task needs by mobile users. In this context, research efforts should focus on predicting mobile user movements and tracking the trajectory so that computing tasks may be offloaded promptly and computation results can be returned to consumers on time.