An efficient Industrial Internet of Things video data processing system for protocol identification and quality enhancement

Abstract

Video has become an essential medium to monitoring, identification and knowledge sharing. For industrial applications, especially Industrial Internet of Things (IIoT), videos encoded with specific protocols are transferred to smart gateways. In a typical IIoT scenario, the protocol of the video is firstly recognised, which prepares for subsequent video tasks. Due to the constrained resources in such scenarios, the video quality can be deteriorated during encoding and compression processes, which is challenging for IIoT. Recently, there have been extensive works focussing on the protocol identification (PI) and video quality enhancement (VQE) tasks on IIoT edge devices using deep neural networks (DNNs). Since DNNs often require high computational resources, complex networks can hardly be deployed on edge devices. An IIoT system which can efficiently identify the stream protocol and enhance the video quality is proposed in this study. The light‐weighted network designs and inference optimisation techniques have been proposed for PI and VQE to realise efficient deployments. Our proposed system employed on an IIoT edge device can achieve an accuracy of higher than 97.52% with fast inference speed for PI. For the VQE task, our system has demonstrated superior performance (15.230 FPS, 0.773 FPS/W) in comparison with the state‐of‐the‐art methods.

Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices

“…Deep neural networks (DNNs) have recently been the focus of substantial studies concentrating on protocol identification (PI) and video quality enhancement (VQE) activities on IIoT edge devices. DNNs frequently need a lot of computing power; sophisticated networks are hardly ever deployable on edge devices [6].…”