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We present a benchmark-driven experimental study of autonomous drone agility relative to edge offload pipeline attributes. This pipeline includes a monocular gimbal-actuated on-drone camera, hardware RTSP video encoding, 4G LTE wireless network transmission, and computer vision processing on a ground-based GPU-equipped cloudlet. Our parameterized and reproducible agility benchmarks stress the OODA (“Observe, Orient, Decide, Act”) loop of the drone on obstacle avoidance and object tracking tasks. We characterize the latency and throughput of components of this OODA loop through software profiling, and identify opportunities for optimization. 

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. This hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable Cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance. Our investigation considers the offload shaping strategies of mapping processes to different computing tiers, gating, and decluttering. We find that all three strategies offer a significant bandwidth savings compared to transmitting full camera images to a cloudlet. Two out of the three devices we test are capable of running all offload shaping strategies within a reasonable latency bound.