Application-aware IoT Camera Virtualization for Video Analytics Edge Computing

Abstract

Video analytics edge computing exploiting IoT cameras has gained high attention. Running such tasks on the network edge is very challenging since video and image processing are both bandwidth-hungry and computationally intensive. Unlike traditional computing systems, IoT cameras are heavily dependent on the environmental factors such as brightness of the view. In this paper, we propose an edge IoT camera virtualization architecture. For this, we leverage an ontology-based application description model and virtualize the IoT camera with container technology that decouples the physical camera and support multiple applications on board. We also develop an IoT camera reconfiguration scheme that allows IoT cameras to dynamically adjust their configuration to environmental context changes without degrading application QoS. Experimental results based on our prototype implementation show that the responsiveness of our system is 2.8x faster than existing approaches in reconfiguring to the environmental context changes.