Joint Deployment and Task Computation of UAVs in UAV-assisted Edge Computing Network

Unmanned Ariel Vehicle(UAV) has been widely used in the edge computing network. Owing to its line-of-sight communication ability and mobility, it has offered task offloading service and some task computation for mobile devices(MDs). Nowadays, as high-intensity calculation application develops rapidly, maximizing the size of offloaded tasks is necessary for meeting the users' experience in applications. In this context, our paper takes research on how to deploy UAVs at the most proper place to offer task offloading with TDMA protocol. Specifically, we will optimize the task offloading number in the MDs-UAV system by combining the location of UAVs, the task computation capacity of UAV, and the MDs-UAV associations in a certain time. We prove that the joint UAV deployment and task computation problem is NP-hard, and use a greedy algorithm to optimize the result. Compared with randomly selected method, our simulation shows that the greedy algorithm performs greatly and the deployment of UAVs are sensible. We believe that this idea will improve the system tasks processing rate and Quality of Experience(QoE) of mobile users.