Deploying Fog Computing in Industrial Internet of Things and Industry 4.0
By Mohammad Aazam, Sherali Zeadally, Khaled A. Harras. IEEE Transactions on Industrial Informatics, 2018
458 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/f89df212a4b1a351ccacb62d35e0479e8d3e7d6a
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In Computer Networks and Communications
Citations in the corpus
- A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective (2021, unknown): “The world is witnessing the start of a new industrial revolution, which is expected to have a profound impact on industries across the globe (Aazam, Zeadally, & Harras, 2018; Soh & Connolly, 2020; Xu, David, & Kim, 2018).”
- Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future (2020, unknown): “General concept of IoT industrial application (Aazam et al., 2018).”
- Computer Science Review (unknown): “These data streams are often complex and require rapid processing to ensure the performance, reliability, and safety of IIoT systems [34].”
- AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives (2022, methodology): “1 3 the fog hierarchy) for making further skilled and powerful technical analysis using complex ML models (Ferrández-Pastor et al. 2018; Aazam et al. 2018).”
- Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges (2020, methodology): “For example, Aazam et al. [40] introduced the architecture of IIoT and industry 4.0, discussed how fog provided computing support in an IIoT environment, as well as some use cases and emerging research challenges.”
- Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, background): “On the contrary, fog computing goes beyond and supports computation offloading of heavy tasks via autonomously and locally operated IIoT nodes, being close to the edge of the network, which in turns leads to decreased network traffic, improved scalability and efficiency, and enhanced security [30].”
- A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities (2020, background): “This is also discussed in [258], where a number of Fog use-cases and research challenges are listed.”