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

Qiu, Tie; Chi, Jiancheng; Zhou, Xiaobo; Ning, Zhaolong; Atiquzzaman, Mohammed; Wu, Dapeng

doi:10.1109/comst.2020.3009103

Cited by 495 publications

(176 citation statements)

References 132 publications

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“…However, the data need time to be transferred to centralized data centres, which degrades the IIoT system efficiency. This implies that processing data on an Edge server could help the IIoT system meet real-time requirements and reduce the decision-making latency [25]. The survey presented in [17] identified two constraints when protecting data confidentiality in IIoT systems through data encryption.…”

Section: Resultsmentioning

confidence: 99%

Searchable Encryption with Access Control in Industrial Internet of Things (IIoT)

Bader

Michala

2021

Wireless Communications and Mobile Computing

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The technological advancements in the Internet of Things (IoT) and related technologies lead to revolutionary advancements in many sectors. One of these sectors, is the industrial sector red that leverages IoT technologies forming the Industrial Internet of Things (IIoT). IIoT has the potential to enhance the manufacturing process by improving the quality, trace-ability, and integrity of the industrial processes. The enhancement of the manufacturing process is achieved by deploying IoT devices (sensors) across the manufacturing facilities; therefore, monitoring systems are required to collect (from multiple locations) and analyse the data, most likely in the cloud. As a result, IIoT monitoring systems should be secure, preserve the privacy, and provide real-time responses for critical decision-making. In this review, we identified a gap in the state-of-the-art of secure IIoT and propose a set of criteria for secure and privacy preserving IIoT systems to enhance efficiency and deliver better IIoT applications.

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Section: Resultsmentioning

confidence: 99%

Searchable Encryption with Access Control in Industrial Internet of Things (IIoT)

Bader

Michala

2021

Wireless Communications and Mobile Computing

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“…Finally, big data and artificial intelligence technologies [79], such as facial recognition or brainwave analysis to know students' attention or emotions are implemented. The Internet of Things is an integration, and we already see functional solutions that combine big data, machine learning, artificial intelligence and cloud computing [6].…”

Section: Discussionmentioning

confidence: 99%

“…The union of these technologies has directly impacted society, with the Internet of Things being a clear example of the ease of everyday tasks using thermostats, surveillance cameras and buttons to dispense toilet paper. This trend is expected to follow an exponential growth in terms of technical change and socioeconomic impact [6,7]. Natural language processor technologies based on machine learning or deep learning are examples providing correction and translation services online.…”

Section: Introduction 1data Revolution: Industry Society and Educationmentioning

confidence: 99%

Educational Warehouse: Modular, Private and Secure Cloudable Architecture System for Educational Data Storage, Analysis and Access

et al. 2021

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Data in the educational context are becoming increasingly important in decision-making and teaching-learning processes. Similar to the industrial context, educational institutions are adopting data-processing technologies at all levels. To achieve representative results, the processes of extraction, transformation and uploading of educational data should be ubiquitous because, without useful data, either internal or external, it is difficult to perform a proper analysis and to obtain unbiased educational results. It should be noted that the source and type of data are heterogeneous and that the analytical processes can be so diverse that it opens up a practical problem of management and access to the data generated. At the same time, ensuring the privacy, identity, confidentiality and security of students and their data is a “sine qua non” condition for complying with the legal issues involved while achieving the required ethical premises. This work proposes a modular and scalable data system architecture that solves the complexity of data management and access. On the one hand, it allows educational institutions to collect any data generated in both the teaching-learning and management processes. On the other hand, it will enable external access to this data under appropriate privacy and security conditions.

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“…2) Efficient FL for Industrial Edge-based IoT Networks: In recent years, industrial edge computing has been applied widely in IoT networks due to its capability to provide instant computation and storage services [173]. The introduction of edge computing in IIoT can also significantly reduce the decision-making latency and save bandwidth resource.…”

Section: Model M2mentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

603

182

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

Cited by 495 publications

References 132 publications

Searchable Encryption with Access Control in Industrial Internet of Things (IIoT)

Searchable Encryption with Access Control in Industrial Internet of Things (IIoT)

Educational Warehouse: Modular, Private and Secure Cloudable Architecture System for Educational Data Storage, Analysis and Access

Federated Learning for Internet of Things: A Comprehensive Survey

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