Balancing Video Analytics Processing and Bandwidth for Edge-Cloud Networks

For IoT networks, signals are captured by edge sensors, system-wide decisions are made at a central host, and processing may be performed at either or both sides of the network. Decision on edge or cloud placement of methods depends in part on processing and bandwidth efficiencies. Video analytics offers a good application to investigate this because signals from edge cameras are large both spatially and temporally, and processing usually involves a sequence of methods. For public camera analytics where processing and bandwidth need only be expended for active periods, we find by experiment a data range for crowd, traffic, and building scenes. For a typical sequence of video analytics methods applied to surveillance, we find upper bounds for processing and bandwidth, and experimental measurements for real - much sparser - video. Explicit description of algorithmic requirements and knowledge of experimentally determined loads gives information to balance methods between edge and cloud.