Edge-Assisted Learning for Real-Time UAV Imagery via Predictive Offloading

Abstract

Real-time decision making with unmanned aerial vehicles (UAVs) imagery is desired in many applications. Deep learning (DL) is a promising enabler for such applications thanks to its recent advancements. However, direct execution of DL models on UAVs, especially small and micro ones, would not only introduce severe delay but also significantly shorten the flight time of UAVs due to the high energy consumption. Realtime transmission of UAV images to ground edge devices for deep analysis can mitigate the computational complexity but may introduce severe interference to ground devices, in addition unpredictable delays due to the dynamic network conditions. To minimize real-time image transmission, this paper designs a new offloading prediction algorithm which first estimates nearfuture need for DL of each UAV and transmit images only when necessary. Holistic resource allocation is made at the edge based on the offloading likelihood analysis of multiple UAVs as well as available resources. Experimental results on real UAV video clips show that our design can save 92% of the communication costs with less than 4% false positive rate.