## 3 UAV Computing  
MEC is an excellent way to solve time-constrained computing problems in mobile IoT devices with limited resources. To reduce latency and backhaul network congestion, the goal is to deliver cloud computing closer to users. This allows processing to be offloaded from centralized data centers to the edge of IoT devices, lowering communication latency and energy consumption, as well as improving real-time decision making and control. Because the IoT system uses many end devices, a huge amount of data is offloaded to the edge devices; this necessitates the development of methods to manage and use the data for various reasons, including self-monitoring, automaintenance, and prediction. Many ML solutions for data processing at the edge are limited because of the significant differences in processing capacity and memory between edge computing and centralized cloud servers. Mobile Edge Computing has lately been examined as an extension of the edge notion. MEC may provide information technology and cloud-computing capabilities to a mobile network, ensuring ultra-low latency and high bandwidth for users [66]. UAVs with communication capabilities, processing, and storage can act as flying MEC at the periphery of the IoT systems. In this scenario, either partially or completely, resource-constrained IoT devices may transfer their computational tasks to MEC-mounted UAVs through LoS communication. Nonetheless, task prediction, UAV deployment, user association, joint resource allocation, and signal processing are hurdles that the system must overcome. For example, UAV cameras acquire a lot of visual data, which the system should evaluate in real time to allow for excellent decision making. Sending data from UAVs to a cloud server would inevitably cause delays. In addition, the deployment of a significant number of IoT devices, such as UAVs, would limit bandwidth, dependability, and security. As a result, processing data at the network edge would result in faster response times, more efficient processing, and reduced network stress. Many strategies for next-generation IoT applications rely on MEC, which is widely recognized as a critical technology. Due to the availability of on-demand communication services and deployment flexibility, UAVs with computing capabilities have grown in popularity. The authors of [11] introduced UAVs to reduce the total of all users' maximum delays in each slot to the small est possible value. The UAV's offloading task ratio, trajectory, and user scheduling factors were optimized. Furthermore, due to the battery technology limitations of mobile devices and UAVs, energy-efficient strategies have received much attention. In [15], the authors investigated how to reduce mobile customers' energy usage. The energy-efficient algorithms of UAVs, which have a substantial impact on the performance of the UAV-enabled MEC system, were not taken into account. The authors of [12] investigated minimizing the weighted aggregate energy consumption of the UAVs and users. The scheduling of computing resources, bandwidth allotment, and the UAV's trajectory were optimized in the minimization problem. The authors in [67] also studied the IoT devices' energy consumption minimization problem in a single UAV-assisted system with time-sensitive tasks. Interestingly, this study proposed the use of the UAV for both task computing and caching. The joint optimization considered the trajectory optimization, computing and communication resources allocated at the UAV, and task offloading decisions made at the IoT devices. UAV-assisted MECs for solving task offloading were discussed in detail by using different techniques [68; 69; 70; 71; 72; 73; 74]. In [75], a UAV-enabled wireless-powered MEC server was investigated to reduce the amount of energy required by the UAV. In a UAV-enabled wireless-powered MEC system, the weighted sum computation bits were maximized in the partial and binary offloading modes [13]. The UAV's energy consumption restriction, on the other hand, was not taken into account in [12; 13; 75].