An Intelligent Blockchain-assisted Cooperative Framework for Industry 4.0 Service Management
Document Type
Article
Publication Title
IEEE Transactions on Network and Service Management
Abstract
The shift towards Industry 4.0 has seen significant steps forward with the advancements in processing, communication, and storage capabilities of Internet of Things (IoT) devices. Cyber-physical systems (CPS) have become more intelligent and withhold advanced processing, storage, and communication capabilities. Rejuvenated network and service management architectures must incorporate the capabilities of intelligent CPS. With that said, this article introduces a cooperative blockchain (BC)-assisted resource and capability sharing approach to fulfill CPS tasks. The solution uses Federated Learning (FL)-enabled Intelligent IoT (IIoT) devices to support Next-Generation Networks (NGNs). A clustering multi-stage blockchain and FL algorithm is used to create local and global models for CPS tasks. Local models are created for each cluster during the first stage. At the second stage, Federated Averaging is used by fog devices to create fog models. A global deep model is then created on the cloud using Federated Aggregation. Blockchain is used to record and validate the added models and ensure that records are not altered under cyber-attacks. Simulation results have shown that the proposed solution outperforms conventional FL and blockchain approaches in terms of accuracy and delay tolerance.
First Page
1
Last Page
1
DOI
10.1109/TNSM.2022.3217395
Publication Date
10-26-2022
Keywords
Blockchain, Blockchains, Computational modeling, Cyber-Physical Systems, Data privacy, Federated Learning, Fourth Industrial Revolution, Industry 4.0, Internet of Things, Next generation networking, Next Generation Networks, Servers
Recommended Citation
I. A. Ridhawi, M. Aloqaily, A. Abbas and F. Karray, "An Intelligent Blockchain-assisted Cooperative Framework for Industry 4.0 Service Management," in IEEE Transactions on Network and Service Management, 2022, doi: 10.1109/TNSM.2022.3217395.
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