Optimal Offloading of Computing-intensive Tasks for Edge-aided Maritime UAV Systems

This paper considers the autonomous detecting and tracking task of the unmanned aerial vehicle (UAV) in the maritime environment. In the maritime UAV tracking system, due to the large size of the image computing-task and the shortage of UAV batteries and computational capability, the UAV needs to offload the computing-intensive task to the edge computing server (ECS) to reduce energy consumption and task latency. However, the task latency is still too long for the UAV tracking algorithm due to the large image size. We research the impact of image resolution on the computing task size and detection accuracy, and formulate an edge-aided UAV system with dynamic image resolution. With the constraint on task latency, we jointly optimize the image resolution, offloading rate, transmission power and local central processing unit (CPU) frequency to minimize energy consumption. Although the proposed problem is non-convex, we transform it into a convex optimization problem through decoupling and problem decomposition, and obtain an optimal offloading strategy. The numerical results show the energy efficiency of the proposed strategy by comparing it with the local first offloading strategy and the edge first offloading strategy.