Data Exchange Mechanism for Real-Time Object Detection in Cloud-Edge IoT System

Real-time target detection based on massive surveillance video data plays a key role in the smart grid, especially in power vision applications. With the indepth construction of power Internet of Things, centralized cloud applications pose challenges to real-time applications due to high bandwidth costs, resulting in delays and loads. Edge computing attempts to perform all the tasks on the edge. However, limited resources on the edge generally cannot guarantee real-time tasks, while complex intelligence algorithms are difficult to fully deploy. Given that both cloud computing solution and edge-only computing solution cannot achieve a good effect, this paper proposes a data exchange mechanism for real-time object detection in a cloud-edge IoT system, which achieves a balance of object detection task executing between delay and accuracy by utilizing the designed intelligent task scheduler. Experimental results performed in real environments using real surveillance video datasets show that by using our cloud-edge collaboration mechanism, the task response time of the system is only about 12% of cloud computing solution while ensuring query accuracy within the practical scope. The system also improves the query accuracy by about 59% compared to edge computing solutions, while the task response time was reduced to its 72%.