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Edge computing offers resource-constrained devices low-latency access to high-performance computing infrastructure. In this paper, we present ePrivateEye, an implementation of PrivateEye that offloads computationally expensive computer-vision processing to an edge server. The original PrivateEye locally processed video frames on a mobile device and delivered approximately 20 fps, whereas ePrivateEye transfers frames to a remote server for processing. We present experimental results that utilize our campus Software-Defined Networking infrastructure to characterize how network-path latency, packet loss, and geographic distance impact offloading to the edge in ePrivateEye. We show that offloading video-frame analysis to an edge server at a metro-scale distance allows ePrivateEye to analyze more frames than PrivateEye's local processing over the same period to achieve realtime performance of 30 fps, with perfect precision and negligible impact on energy efficiency.
Streiffer et al. (Thu,) studied this question.