Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review

A Miglani, N Kumar - Computer Communications, 2021 - Elsevier
Computer Communications, 2021Elsevier
Keeping in view of the constraints and challenges with respect to big data analytics along
with security and privacy preservation for 5G and B5G applications, the integration of
machine learning and blockchain, two of the most promising technologies of the modern era
is inevitable. In comparison to the traditional centralized techniques for security and privacy
preservation, blockchain uses decentralized consensus algorithms for verification and
validation of different transactions which are supposed to become an integral part of …
Abstract
Keeping in view of the constraints and challenges with respect to big data analytics along with security and privacy preservation for 5G and B5G applications, the integration of machine learning and blockchain, two of the most promising technologies of the modern era is inevitable. In comparison to the traditional centralized techniques for security and privacy preservation, blockchain uses decentralized consensus algorithms for verification and validation of different transactions which are supposed to become an integral part of blockchain network. Starting with the existing literature survey, we introduce the basic concepts of blockchain and machine learning in this article. Then, we presented a comprehensive taxonomy for integration of blockchain and machine learning in an IoT environment. We also explored federated learning, reinforcement learning, deep learning algorithms usage in blockchain based applications. Finally, we provide recommendations for future use cases of these emerging technologies in 5G and B5G technologies.
Elsevier
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