## 1 Introduction  
Recently, with the rapid development of the Internet of Everything (IoE), a massive range of new applications and services, such as Augmented Reality (AR), Virtual Reality (VR), e-health, and face recognition have emerged and interacted with each other [1]. These applications begin to place greater demands on computing power, network throughput, and latency in order to provide a better Quality of Service (QoS) [2] and satisfy the Quality of Experience (QoE) [3]. The significant computing workload and storage requirements,on the other hand, place a strain on resource-constrained Internet of Things (IoT) devices. To solve this problem, Mobile Edge Computing (MEC) has been proposed as a paradigm that involves installing servers with rich services closer to IoT devices in smart environments, which can significantly enhance QoS, improve QoE, and minimize energy consumption [4; 5]. However, the MEC comprises of infrastructure at fixed locations, preventing it to move closer to IoT devices in places with no communication coverage (e.g., harsh environments) or in particular instances when MEC deployment is difficult. Aerial access networks are crucial enablers for future (e.g., B5G) wireless systems [6; 7]. Aerial access networks comprise various platforms with a varying range of coverage, including low-altitude platforms, high-altitude platforms, and satellite communications. Furthermore, various characteristics of aerial access networks distinguish them, such as ubiquity, mobility, availability, simultaneity, and scalability [6]. These fundamentals will support the complete range of traditional terrestrial communications and enhance communication services and customer delight. Unmanned Aerial Vehicles (UAVs) are used as Access Points (APs) and radio towers at the low-altitude tier of supporting B5G aerial access networks, providing wireless and computing services to ground users from the air when the terrestrial communication network is unavailable [8]. UAVs can establish wireless networks as mobile Base Stations (BSs) in a short amount of time. In addition, UAVs can fly about to adjust their positions to provide Line-of-Sight (LoS) connectivity to the ground nodes, which can greatly enhance network performance compared with terrestrial communications. Furthermore, UAVs can be swiftly deployed to provide wireless communications in remote places with complicated conditions, such as disaster areas and emergency events [9; 10], because of their flexibility and mobility. Moreover, UAVs with computer servers provide a new MEC paradigm. Therefore, UAVs have received much attention from the business world and researchers. However, critical technological hurdles in UAV computing, e.g., limited operating times, are yet to be overcome, and it is in the early stage. A UAV equipped with an MEC can quickly respond to communication and processing demands. Recently, the MEC with UAV capability has received much interest. The UAV trajectory, the offloading task ratio, and the user scheduling variables were optimized together [11] to reduce the total of all users' maximum delays. The authors of [12] investigated the weighted energy consumption reduction issue in a UAV-assisted MEC architecture, in which a UAV serves as a computational server or a relay for different offloading user workloads to the access point. In addition, the UAV's processing bandwidth allocation, resources scheduling, and trajectory design were all optimized concurrently. The authors of [13] introduced the difficulties of computation rate maximization in a wirelessly powered UAV-enabled MEC system with partial and binary computation offloading. Figure 1 shows an MEC with UAV capability. The UAV receives data from the ground users, such as game data, facial recognition data, environmental monitoring data, AR and VR data, etc. After receiving data from ground users, the UAV begins processing and computing.