EdgeLeague: Camera Network Configuration With Dynamic Edge Grouping for Industrial Surveillance

Object detection is crucial for surveillance in edge-enabled Industrial Internet-of-Things. Massive high-dimensional video streams without considering priority differences connect to edges via narrow and time-varying uplink channels, which should be analyzed efficiently for accurate and fast surveillance responses. However, time-varying network environments and constrained edge resources degrade surveillance's accuracy and real-time performance. This article proposes EdgeLeague for multiple video streams with different quality of service, which maintains high surveillance performance under edge resource limitations and uplink bandwidth dynamics by edge collaboration and camera network configuration. The EdgeLeague scheme is formulated by an NP-hard integer nonlinear problem to dynamically configure camera network resolutions and detection models on cooperative edges. To accelerate configuration responses, the formulated problem is decomposed into edge league grouping, video-league matching, and video configuration, solved by low-complexity algorithms. Theoretical analysis is provided for optimal video-league matching. Simulations show EdgeLeague achieves 0.312 s latency and 86.3% surveillance accuracy.