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Secure Data Sensing, Computing, and Dissemination in Vehicular Ad Hoc Networks

Authors: Yingjie Xia, Xuejiao Liu, Huihui Wu, Qichang Li

Publisher: Springer Nature Singapore

Book Series : Advanced Topics in Science and Technology in China

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Table of Contents

About this book

This book focuses on the fields of security of vehicular ad hoc networks (VANETs). Building upon previous research findings, it conducts research on three modules: secure data sensing, computing, and dissemination in VANETs. The book adopts a combination of security analysis, theoretical analysis, and simulation analysis to comprehensively evaluate and demonstrate the effectiveness and performance of proposed solutions. It aims to assist other researchers in conducting studies on the data security issues of vehicular ad hoc networks, while designing secure and efficient schemes.

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Table of Contents

Frontmatter

Chapter 1. Vehicular Ad Hoc Networks and Security Challenges

Abstract

This chapter provides an overview of Vehicular Ad Hoc Networks (VANETs), highlighting their role in enabling communication between vehicles and infrastructure to support intelligent transportation systems. It also examines key security challenges, including data integrity, authentication, and privacy preservation, which are critical for the deployment of VANETs.

Yingjie Xia, Xuejiao Liu, Huihui Wu, Qichang Li

Chapter 2. Secure Vehicular Crowdsensing and Malicious Vehicles Detection in VANETs

Abstract

Vehicular crowdsensing networks play a critical role in the Internet of Vehicles by enabling efficient information services, but challenges like on-demand message authentication and privacy protection persist. We propose a secure crowdsensing scheme, TRAMS, based on multi-authority attribute-based signatures, enabling fine-grained policies for participant authentication while safeguarding vehicle privacy. TRAMS incorporates a multi-authority key management system to enhance sensing efficiency, achieving superior message authentication compared to single-authority systems. To address malicious behaviors in VANETs, we introduce HDRS, a hybrid reputation system where vehicles and roadside units independently evaluate reputations and cross-reference results. HDRS leverages a reliability module and a dynamic adjustment mechanism to counter intelligent attacks, achieving up to 30% higher detection rates for collusion and 16% for adaptive threats compared to existing solutions.

Yingjie Xia, Xuejiao Liu, Huihui Wu, Qichang Li

Chapter 3. Efficient and Privacy-Preserving Authentication in VANETs

Abstract

Efficient and secure authentication is crucial for verifying message integrity and sender identity in VANETs, especially across multiple trust domains. We propose two advanced solutions: CD-BASA and EPP-GAS. CD-BASA utilizes blockchain, accumulators, and interplanetary file systems to enable streamlined cross-domain batch authentication, significantly reducing identity verification costs with a novel multi-accumulator batch proof algorithm, improving authentication overhead by 18.2% and proof cost by 69.9%. EPP-GAS focuses on vehicle platooning (VP), employing an extended blockchain model and Shamir’s threshold scheme for anonymous group authentication, achieving 31% higher efficiency in cross-domain group authentication and 43.9% lower communication overhead. Both schemes outperform existing methods in security, efficiency, and resource utilization.

Yingjie Xia, Xuejiao Liu, Huihui Wu, Qichang Li

Chapter 4. Traffic Data Quality Improvement and Secure Outsourcing Computing in VANETs

Abstract

Traffic data quality and secure computation are critical for intelligent transportation systems (ITS). To address low-quality traffic data, we propose STAP, a spatio-temporal correlation model using an improved Random Forest for anomaly detection and XGBoost for data estimation, effectively enhancing data quality as validated by experiments on real-world data from Changsha, China. For secure outsourcing computing in vehicular fog environments, we introduce SE-VFC, which combines lightweight BLS and group signatures for anonymous batch authentication and privacy protection of fog vehicles, ensuring correctness and traceability of computations while maintaining low overhead. Compared to traditional anomaly detection methods, STAP improves average accuracy by 5%, while SE-VFC proves effective and practical in vehicular fog computing, offering low communication and computation overhead.

Yingjie Xia, Xuejiao Liu, Huihui Wu, Qichang Li

Chapter 5. Traffic Data Access Control in VANETs

Abstract

Ensuring secure and reliable information dissemination across multi-RSU in VANETs is essential for effective V2I communication. To address challenges like incomplete data transmission and dynamic network restructuring, we propose a fine-grained access control scheme leveraging ciphertext-policy attribute-based encryption (CP-ABE). Our scheme integrates proxy re-encryption to ensure seamless access to encrypted data for high-speed vehicles across RSUs, while maintaining privacy. To address computational limitations of on-board units, we introduce a CP-ABE delegation scheme that offloads complex computations to RSUs, optimizing efficiency through a decision tree-based approach. Experimental results demonstrate improved reliability, privacy preservation, and efficiency for multimedia data dissemination in cross-RSU scenarios，making it suitable for dynamic and high-demand VANETs environments

Yingjie Xia, Xuejiao Liu, Huihui Wu, Qichang Li

Chapter 6. Accurate Policy Enforcement for Secure Data Dissemination in VANETs

Abstract

Achieving accurate and secure data dissemination in dynamic VANETs requires expressive and conflict-free access control policies. To address this, we propose a policy enforcement framework that enables high-mobility vehicles and RSUs to co-design access control policies using disjunctive normal form (DNF) for flexibility and accuracy. Conflicts are resolved through confidence-weight-based mechanisms. Additionally, we introduce RLID-V, a reinforcement learning-based policy generation scheme that dynamically updates confidence weights and incorporates decision tree-based feedback for continuous improvement. Experiments in traffic guidance and accident warning scenarios demonstrate enhanced accuracy, robustness, and negligible delay overhead compared to existing methods, making this approach a reliable solution for secure data dissemination in VANETs.

Yingjie Xia, Xuejiao Liu, Huihui Wu, Qichang Li

Chapter 7. Conclusions

Abstract

This chapter summarizes the key findings and contributions of the book, highlighting advanced techniques for secure data sensing, computing, and dissemination in VANETs. It also discusses future directions, emphasizing the integration of blockchain and AI to address emerging challenges in large-scale, high-mobility vehicular networks.

Yingjie Xia, Xuejiao Liu, Huihui Wu, Qichang Li

Title: Secure Data Sensing, Computing, and Dissemination in Vehicular Ad Hoc Networks
Authors: Yingjie Xia
Xuejiao Liu
Huihui Wu
Qichang Li
Copyright Year: 2025
Publisher: Springer Nature Singapore
Electronic ISBN: 978-981-9603-36-7
Print ISBN: 978-981-9603-35-0
DOI: https://doi.org/10.1007/978-981-96-0336-7