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Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges
IEEE Communications Surveys & Tutorials ( IF 34.4 ) Pub Date : 2020-01-01 , DOI: 10.1109/comst.2020.3009103
Tie Qiu , Jiancheng Chi , Xiaobo Zhou , Zhaolong Ning , Mohammed Atiquzzaman , Dapeng Oliver Wu

The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

中文翻译:

工业物联网中的边缘计算:架构、进步和挑战

工业物联网 (IIoT) 是由物联网 (IoT) 催生的重要研究领域。IIoT通过网络连接各类工业设备;建立数据采集、交换和分析系统;并优化流程和服务,从而降低成本并提高生产力。在工业物联网中引入边缘计算可以显着降低决策延迟,节省带宽资源,并在一定程度上保护隐私。本文概述了工业物联网边缘计算的研究进展。首先讨论了工业物联网和边缘计算的概念,然后详细讨论和总结了边缘计算的研究进展。接下来,从边缘计算的角度提出了未来工业物联网的架构,以及它在路由方面的技术进展,分析了任务调度、数据存储和分析、安全性和标准化。此外,我们在基于 5G 的边缘通信、负载平衡和数据卸载、边缘智能以及数据共享安全方面讨论了 IIoT 中边缘计算的机遇和挑战。最后,我们介绍了边缘计算在 IIoT 中的一些典型应用场景,例如预测与健康管理 (PHM)、智能电网、制造协同、智能网联汽车 (ICV) 和智能物流。
更新日期:2020-01-01
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