Towards Dynamically Reconfigurable IoT Camera Virtualization for Video Analytics Edge Cloud Services

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 bandwidth-hungry and computationally intensive. IoT cameras are heavily dependent on environmental factors such as the brightness of the view. In this paper, we propose an edge IoT camera virtualization architecture that enables an IoT camera to accommodate multiple application operation semantics and dynamically adjust its configuration to preserve them in the presence of environment context changes. For this, we develop an ontology-based application description model, a virtualization architecture with the container technology, and a context-aware dynamic reconfiguration scheme.