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Navigating the Future of Secure and Efficient Intelligent Transportation Systems using AI and Blockchain



Jyotsna Ghildiyal Bijalwan1, Jagendra Singh2, Vinayakumar Ravi6, *, Anchit Bijalwan3, Tahani Jaser Alahmadi7, *, Prabhishek Singh2, Manoj Diwakar4, 5
1 School of Business, British University Vietnam, Hanoi, Vietnam.
2 School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India
3 School of Computing and Innovative Technologies, British University Vietnam, Hanoi, Vietnam
4 Department of CSE, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
5 Graphic Era Hill University, Dehradun, India
6 Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
7 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia


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Creative Commons License
© 2024 The Author(s). Published by Bentham Open.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to these authors at the Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia and Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; E-mails: vinayakumarr77@gmail.com and tjalahmadi@pnu.edu.sa


Abstract

Introduction/Background

This study explores the limitations of conventional encryption in real-world communications due to resource constraints. Additionally, it delves into the integration of Deep Reinforcement Learning (DRL) in autonomous cars for trajectory management within Connected And Autonomous Vehicles (CAVs).

This study unveils the resource-constrained real-world communications, conventional encryption faces challenges that hinder its feasibility. This introduction sets the stage for exploring the integration of DRL in autonomous cars and the transformative potential of Blockchain technology in ensuring secure data transfer, especially within the dynamic landscape of the transportation industry.

Materials and Methods

The research methodology involves implementing DRL techniques for autonomous car trajectory management within the context of connected and autonomous CAVs. Additionally, a detailed exploration of Blockchain technology deployment, consensus procedures, and decentralized data storage mechanisms.

Results

Results showcase the impracticality of conventional encryption in resource-constrained real-world communications. Moreover, the implementation of DRL and Blockchain technology proves effective in optimizing autonomous car subsystems, reducing training costs, and establishing secure, globally accessible government-managed transportation for enhanced data integrity and accessibility.

Discussion

The discussion delves into the implications of the study's findings, emphasizing the transformative potential of DRL in optimizing autonomous car subsystems. Furthermore, it explores the broader implications of Blockchain technology in revolutionizing secure, decentralized data transfer within the transportation industry.

Conclusion

In conclusion, the study highlights the impracticality of conventional encryption in real-world communications and underscores the significant advancements facilitated by DRL in autonomous vehicle trajectory management. The integration of Blockchain technology not only ensures secure data transfer but also paves the way for a globally accessible transportation blockchain, reshaping the future landscape of the industry.

Keywords: Internet of things (IoT), Connected vehicles (CV), Internet of vehicles (IoV), Cognitive internet of vehicles (CIoV), Connected and automated vehicles (CAVs), AI.