Smart and secure CAV networks empowered by AI-enabled blockchain: the next frontier for intelligent safe driving assessment

Xia, L. , Sun, Y. , Swash, R., Mohjazi, L. , Zhang, L. and Imran, M. A. (2022) Smart and secure CAV networks empowered by AI-enabled blockchain: the next frontier for intelligent safe driving assessment. IEEE Network, 36(1), pp. 197-204. (doi: 10.1109/MNET.101.2100387)

Abstract

Securing safe driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern, despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Diverse malicious network attacks are ubiquitous, along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. In this article we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), which offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes. Finally, we discuss several open challenges that need to be addressed in future CAV networks.

• Item Type: Articles
• Additional Information: This work was supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Xia, Le and Zhang, Professor Lei and Swash, Professor Rafiq and Sun, Dr Yao and Imran, Professor Muhammad and Mohjazi, Dr Lina
• Authors: Xia, L., Sun, Y., Swash, R., Mohjazi, L., Zhang, L., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Network
• Publisher: IEEE
• ISSN: 0890-8044
• ISSN (Online): 1558-156X
• Published Online: 02 March 2022
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Network 36(1): 197-204
• Publisher Policy: Reproduced in accordance with the publisher copyright policy