## 2 Related Work  
Research on task offloading of UAVs can be divided into two types. Studies of the first type equip UAVs with process units to handle the tasks from user equipment. In [10], a UAV-enabled computing system is considered in which the UAV not only provides energy supply to end users, but also provides edge computing services for the uploaded data from end users. The problem of how to maximize the MEC computing speed is studied. [PERSON] et al. [11] investigate a network system with several UAVs, in which not only the UAVs but also the user equipment can perform tasks generated by the end users. With the aim of decreasing the energy cost of the UAVs, a method that jointly optimizes energy control, computing power allocation, and task execution delay is proposed. To provide sufficient computing resources for ground mobile devices, [PERSON] et al. [12] suggest that UAVs should equipped with powerful computing units. In [13], the authors believe that UAVs can be used as mobile clouds to complete tasks offloaded by mobile terminals with limited processing capabilities. The evaluation comparisons indicate the high achievement of the scheme presented in this paper. [PERSON] et al. [14] propose that the tasks generated by sensor devices can be completed by the cooperation of their nearby idle sensors and the UAV installed with a MEC server. [PERSON] et al. [15] introduce a system consisting of an access point, an UAV, and many ground user devices. UAVs can perform the tasks uploaded by user equipment and can also relay task data to access points for processing. Inspired by the fact that UAVs can communicate with each other, [PERSON]. [16] proposes a UAV-based wireless energy transfer system. However, because the energy power of the UAV is restricted, a continuous growth in the number of tasks leads to a continuous decline in the success ratio of task execution. Research studies of the second type aim to execute tasks with the help of remote clouds or end users' nearby edge servers. For example, in [17], data are transmitted between the terminal and the access device via an UAV. The UAV can also transmit task data from the source device to the edge for timely processing. [PERSON]. [18] combines edge computing with UAVs. Security problems in the task execution process are presented. The authors in [7] describe an energy-saving computation offloading scheme for UAV-MEC systems with emphasis on physical-layer security. The authors in [19] combine an UAV with edge computing. The UAV collects data from Internet of Thing (IoT) devices and later transmits data to access points, which perform their best to compute and process data. To minimize both the energy exhaustion of IoT devices and the energy spent on data offloading of the UAV, [PERSON]. [20] proposes a UAV-aid MEC paradigm. The experimental results indicate the paradigm remarkably reduces the energy utilization compared to other existing strategies. To optimize the task execution latency of the fog computing system with one UAV, [PERSON] et al. [21] present a scheduling algorithm and a multi-task offloading scheme under a multi-server environment. The authors in [22] jointly optimize computational task offloading and resource assignment. The authors in [23] suggest that the data collected by UAVs can be transferred to the edge server which is connected to the base station via high-speed wired links. A smart uploading mechanism specifically developed for the UAV-assisted MEC network was designed. In [24], to obtain good system utility, the authors propose that a single application can be split into multiple tasks and uploaded to the server node for further analyzing. To facilitate disaster rescue, the authors in [25] present a network consisting of two subnetworks, which are an aerial subnetwork formed by multiple UAVs and a ground vehicular network formed by automobiles. Three kinds of communications are introduced to deliver data efficiently. Ref.