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Cited by 275

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Edge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things

Zhang, Ke, Zhu, Yongxu, Maharjan, Sabita, Zhang, Yan·2019·IEEE Network

Abstract

Edge intelligence is a key enabler for IIoT as it offers smart cloud services in close proximity to the production environment with low latency and less cost. The need for ubiquitous communication, computing, and caching resources in 5G beyond will lead to a growing demand to integrate heterogeneous resources into the edge network. Furthermore, distributed edge services can make resource transactions vulnerable to malicious nodes. Ensuring secure edge services under complex industrial networks is a big challenge. In this article, we present an edge intelligence and blockchain empowered IIoT framework, which achieves flexible and secure edge service management. Then we propose a cross-domain sharing inspired edge resource scheduling scheme and design a credit-differentiated edge transaction approval mechanism. Numerical results indicate that the proposed schemes bring significant improvement in both edge service cost and service capacities.

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Metadata

Authors
Zhang, K., Zhu, Y., Maharjan, S., & Zhang, Y.
Venue
IEEE Network
Published
2019-09
Volume
33
Issue
5
Pages
12-19
ISSN
  • 0890-8044 (ISSN-L)
  • 1558-156X
OpenAlex
W2979680647
Cited by
275
References
0
FWCI
24.92
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Edge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things. https://app.cochat.ai/papers/10.1109/mnet.001.1800526
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Zhang, K., Zhu, Y., Maharjan, S., & Zhang, Y. (2019). Edge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things. IEEE Network. https://doi.org/10.1109/mnet.001.1800526

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.