CANS: Communication Limited Camera Network Self-Configuration for Intelligent Industrial Surveillance
This work addresses real-time video surveillance challenges in edge-enabled industrial IoT, offering a solution for managing multiple video streams with heterogeneous QoS demands, though it is incremental as it builds on prior single-camera configuration methods.
The paper tackles the problem of communication congestion in camera networks for industrial surveillance, which delays and degrades vision detection tasks, by proposing an adaptive self-configuration method (CANS) that formulates the trade-off between accuracy and latency as an optimization problem. Simulation results show CANS achieves low end-to-end latency (13 ms on average) with high accuracy (92% on average) under network dynamics.
Realtime and intelligent video surveillance via camera networks involve computation-intensive vision detection tasks with massive video data, which is crucial for safety in the edge-enabled industrial Internet of Things (IIoT). Multiple video streams compete for limited communication resources on the link between edge devices and camera networks, resulting in considerable communication congestion. It postpones the completion time and degrades the accuracy of vision detection tasks. Thus, achieving high accuracy of vision detection tasks under the communication constraints and vision task deadline constraints is challenging. Previous works focus on single camera configuration to balance the tradeoff between accuracy and processing time of detection tasks by setting video quality parameters. In this paper, an adaptive camera network self-configuration method (CANS) of video surveillance is proposed to cope with multiple video streams of heterogeneous quality of service (QoS) demands for edge-enabled IIoT. Moreover, it adapts to video content and network dynamics. Specifically, the tradeoff between two key performance metrics, \emph{i.e.,} accuracy and latency, is formulated as an NP-hard optimization problem with latency constraints. Simulation on real-world surveillance datasets demonstrates that the proposed CANS method achieves low end-to-end latency (13 ms on average) with high accuracy (92\% on average) with network dynamics. The results validate the effectiveness of the CANS.