# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 4 UAV-Enabled MEC and Assisted MEC Based on AI
Nevertheless, the system faces many challenges related to task prediction, UAV deployment, users' association, signal processing, and joint resource allocation. Considering the EC architecture, UAVs with flexibility and mobility are likely to deliver decentralized solution perspectives significantly. The flying edge architecture is intended to improve the CC capabilities of latency-sensitive and real-time IoT applications. This enables computing offloading at the edge of IoT devices rather than centralized data centers, reducing communication latency and facilitating real-time decision-making and control. Since the IoT system operates with many end-devices, a large quantity of data is offloaded to the edge devices; this requires developing mechanisms that manage and exploit the data for various purposes, such as auto-maintenance, self-monitoring, and prediction. There is a considerable difference between EC devices and centralized cloud servers regarding their processing capacities and memory, limiting multiple AI technologies for data processing at the edge. Therefore, numerous criteria should be considered when incorporating any AI technique to profit from their robustness, due to memory capacity and energy consumption constraints [111]. EC delivers resources in a decentralized manner, allowing faster addressing of user demands than a regular CC, even with limited computational capability. Various challenges arise, such as task scheduling, resource allocation, and offloading, substantially influencing the overall performance. Nevertheless, most of the proposed solutions are characterized by non-convexity and complexity, as they enable continuous learning ability for the dynamic environment and make a real-time inference with low computational complexity. Applying AI to networking challenges has gained popularity over the past few decades. For instance, ML has been widely used in the networking domain due to its ability to interact with complex environments and make decisions. It may also provide eminent solutions to improve network performance in many subdomains, such as resource allocation, network traffic prediction and classification, congestion control, and routing [32]. In most UAV-enabled MEC architectures, a controller called the follow-me cloud (FMC) collects information about overall system states (i.e., users, UAVs, and MEC servers). This control unit is in charge of managing offloading activities using AI technologies. Figure 5 displays an example of a cloudlet mounted on a UAV for a UAV-assisted MEC system. The UAV receives offloaded tasks from an IoT device and sends the results back upon fulfillment of the task's execution. Furthermore, the UAV could transport the received data to the nearest ground servers in case of complex processing requirements that overfit the onboard cloudlet's capabilities. The system may contain a set of UAVs that serve a massive area of deployed IoT devices that include mobile phones, sensors, vehicles, and robots. The data generated by the users is analyzed and treated via onboard cloudlets with the help of the AI approach. As shown in Table 5, the DRL, DL, and GA can be utilized to solve task offloading challenges such as energy consumption limits, processing, and latency. Furthermore, for resource allocation management issues, the algorithms RL, DRL, GA, and RL-ACO might be utilized to regulate the energy consumption in UAVs and reduce the latency and reaction time. Moreover, the later algorithms, DE and ACO, can be applied to address joint optimization difficulties, whereas DL may enhance UAVs' security and path planning performance. MEC networks should offer seamless connection, meet the QoS needs of many sensors, and handle a vast volume of data created by physical surroundings. AI approaches offering robust analysis, learning, optimizing, and intelligent recognition capabilities may be integrated into UAV-enabled MEC systems to intelligently carry out performance optimization, information discovery, advanced learning, structure organization, and complex decision-making.