Distributed Collaborative Execution on the Edges and Its Application to AMBER Alerts

In the Internet of Everything era, billions of geographically distributed things will connect to the Internet and generate hundreds of zettabytes of data per year. Pushing that data to the cloud requires tremendous network bandwidth cost and latency. This is too onerous for some latency-sensitive applications, such as vehicle tracking using city-wide cameras. One application currently limited by such obstacles is the America’s Missing Broadcast Emergency Response (AMBER) Alert system—but edge computing could transform this system’s capabilities. Edge computing is a new computing paradigm that greatly diminishes data transmission and response latency by processing data at the proximity of data sources. However, most vision-based analytics are compute-intensive, and an edge device might be overwhelmed given tens of frames each second for real-time analysis. Also, the system needs a customized and flexible interface to implement efficient tracking strategies. To meet these needs, here we extend a big data processing framework, called Firework, to support collaboration between multiple edge devices and customizable task-scheduling strategies. Based on this extended version of Firework, we implement the AMBER alert assistant (A3), which efficiently tracks and locates a vehicle by analyzing city cameras’ data in real time. We also propose two kinds of customized task-scheduling algorithms for vehicle tracking in A3. Comprehensive evaluation results show that A3 achieves real-time video analytics by collaborating among multiple edge devices; and the proposed location-direction-related diffusion strategy effectively controls the searching area for vehicle tracking by smartly selecting candidate cameras.