Edge Computing in 5G for Drone Navigation: What to Offload?
Samira Hayat, Roland Jung, Hermann Hellwagner, Christian Bettstetter, Driton Emini, Dominik Schnieders
Abstract
Small drones that navigate using cameras may be limited in their speed and agility by low onboard computing power. We evaluate the role of edge computing in 5G for such autonomous navigation. The offloading of image processing tasks to an edge server is studied with a vision-based navigation algorithm. Three computation modes are compared: onboard, fully offloaded to the edge, and partially offloaded. Partial offloading is expected to pose lower demands on the communication network in terms of transfer rate than full offloading but requires some onboard processing. Our results on the computation time help select the most suitable mode for image processing, i.e., whether and what to offload, based on the network conditions.