Ayaz, Ferheen, Sheng, Zhengguo, Tian, Daxin, Nekovee, Maziar and Saeed, Nagham ORCID: https://orcid.org/0000-0002-5124-7973 (2022) Blockchain-empowered AI for 6G-enabled Internet of Vehicles. Electronics, 11 (20). p. 3339.

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Abstract

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network.

Item Type:	Article
Identifier:	10.3390/electronics11203339
Keywords:	blockchain; federated learning; Internet of Vehicles; security; privacy; AI
Subjects:	Construction and engineering > Electrical and electronic engineering
Related URLs:	Publisher https://zenodo.org/record/5575863#.Y2PqM...
Depositing User:	Nagham Saeed
Date Deposited:	15 Oct 2022 10:26
Last Modified:	04 Nov 2024 11:20
URI:	https://repository.uwl.ac.uk/id/eprint/9441

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