Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges

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...

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Vydáno v:IEEE Communications surveys and tutorials Ročník 22; číslo 4; s. 2462 - 2488
Hlavní autoři: Qiu, Tie, Chi, Jiancheng, Zhou, Xiaobo, Ning, Zhaolong, Atiquzzaman, Mohammed, Wu, Dapeng Oliver
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.01.2020
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ISSN:2373-745X
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Shrnutí: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.
ISSN:2373-745X
DOI:10.1109/COMST.2020.3009103