EdgeStreaming: Secure Computation Intelligence in Distributed Edge Networks for Streaming Analytics

Abstract

In modern information systems, real-time streaming data are generated in various vertical application scenarios, such as industrial security cameras, household intelligent devices, mobile robots and among others. However, these low-end devices can hardly provide real-time and accurate data analysis functionalities due to their limited on-board performances. Traditional centralized server computing also suffers from its prolonged transmission latency, resulting in huge response time. To deal with this problem, this paper presents a novel distributed computation intelligent system with nearby edge devices, abbreviated as EdgeStreaming, to facilitate rapid and accurate analysis of streaming data. Firstly, we thoroughly explore the available edge devices surrounding the terminal to generate an internally interconnected edge network. This edge network real-time perceives and updates the internal resource status of each edge device, such as computational and storage resources. Dynamic allocation of external computational or storage demands can be made based on the current load of individual edge devices. Consequently, the streaming data perceived by external terminal devices can be transmitted in real-time to any edge gateway. The edge network employs a well-designed task scheduling strategy to partition and allocate streaming data processing demands to one or multiple edge devices. Additionally, it customizes computational requirements judiciously, for instance, by utilizing model compression to expedite computation speed. We deployed an edge network comprising multiple Raspberry Pis, NVIDIA Jetson Nano, and Jetson NVIDIA TX2 devices, successfully achieving real-time analysis and detection of video streaming data. We believe our work provides new technological support for the real-time processing of streaming data.