The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Deploying Fog Computing in Industrial Internet of Things and Industry 4.0
Abstract
Rapid technological advances have revolutionized the industrial sector. These advances range from automation of industrial processes to autonomous industrial processes, where a human input is not required. Internet of Things (IoT), which has emerged a few years ago, has been embraced by industry, resulting in what is known as the Industrial Internet of Things (IIoT). IIoT refers to making industrial processes and entities part of the Internet. Restricting the definition of IIoT to manufacturing yields another subset of IoT, known as Industry 4.0. IIoT and Industry 4.0, will consist of sensor networks, actuators, robots, machines, appliances, business processes, and personnel. Hence, a lot of data of diverse nature would be generated. The industrial process requires most of the tasks to be performed locally because of delay and security requirements and structured data to be communicated over the Internet to web services and the cloud. To achieve this task, middleware support is required between the industrial environment and the cloud/web services. In this context, fog is a potential middleware that can be very useful for different industrial scenarios. Fog can provide local processing support with acceptable latency to actuators and robots in a manufacturing industry. Additionally, as industrial big data are often unstructured, it can be trimmed and refined by the fog locally, before sending it to the cloud. We present an architectural overview of IIoT and Industry 4.0. We discuss how fog can provide local computing support in the IIoT environment and the core elements and building blocks of IIoT. We also present a few interesting prospective use cases of IIoT. Finally, we discuss some emerging research challenges related to IIoT.
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Cited by
Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges
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BackgroundTackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
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Towards edge computing in intelligent manufacturing: Past, present and future
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Metadata
- Authors
- Aazam, M., Zeadally, S., & Harras, K. A.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2018-10
- Volume
- 14
- Issue
- 10
- Pages
- 4674-4682
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2846676623
- Cited by
- 567
- References
- 16
- FWCI
- 44.19
- Type
- journal article
- Language
- en
- Open access
- closed
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