Abstract
Multi-Unmanned Aerial Vehicle (UAV) systems with high-resolution cameras have been found to be useful for operations such as disaster management and smart farming. These systems feature Flying Ad-Hoc Networks (FANETs) that connect the computation edge with UAVs and a Ground Control Station (GCS) through air-to-ground network links. Leveraging the edge computation resources effectively with energy-awareness, and dealing with intermittent failures of FANET links are the major challenges in supporting video processing applications. In this paper, we propose a novel energy-aware dynamic computation offloading scheme for UAV systems, which provides the ability to intelligently share tasks among individual UAVs and allows for parallel execution of tasks while evenly distributing energy consumption. Intelligence gathering is performed using machine learning to create resource consumption profiles for a given set of video processing tasks prior to scheduling. Our scheme handles the problem of computation offloading tasks as a job-shop scheduling problem where we aim to minimize the total energy consumption in the edge resources while minimizing video processing times to meet application requirements. Our experimental results show our energy-aware dynamic offloading scheme enables lower processing time for low drone-to-ground server ratios and consumes less energy when compared to other offloading schemes. Notably, these results also hold in various other multi-UAV scenarios involving largely different number of detected objects.
