The Internet of Vehicles (IoV) is changing the contemporary mobility, as it allows real-time communication between vehicles, infrastructure, and cloud services. Nevertheless, such growing connectivity brings on serious privacy, regulatory, and trust issues especially because sensitive behavioral and location information is exposed. The current IoV-security systems tend to be based on identity-based checks, or centralized trust authorities, which can lead to infringement of user privacy and cause surveillance and profiling threats. The paper is inspired by privacy-preserving architectures in the Metaverse to suggest a decentralized trust system of IoV systems on the basis of zero-knowledge proofs, namely zk-SNARKs. The suggested solution allows vehicles to cryptographically verify that they meet regulatory or operational regulations- i.e. valid insurance, safety test, or emissions- without revealing personal identifiers or raw information. The framework enables building scalable, low-latency and audible trusts and following data minimization principles through combining zk-SNARK verification and Layer 2 blockchain solutions.
As autonomous driving technology advances towards Level 4 and 5, virtual simulation has become an indispensable tool for safety validation. However, a critical ''trust gap'' exists between developers and regulators; submitting raw simulation logs poses risks of data tampering (integrity issues) and intellectual property leakage (privacy issues). To address this, we propose ZK-V, a universal, privacy-preserving verification framework designed to interface with various autonomous driving simulators. By leveraging Zero-Knowledge Proofs and blockchain technology, ZK-V allows a Prover to cryptographically demonstrate that a simulation run adhered to specific safety constraints such as collision avoidance and speed limits without revealing the underlying telemetry data. This paper outlines the simulation-agnostic system architecture and constraint logic, offering a scalable solution for decentralized, trust-free certification in the Web 4.0 mobility era.
Abstract The integration of connected vehicles into 5G networks introduces stringent requirements in terms of latency, reliability, security, and adaptability that are not fully addressed by existing 5G architectures. In particular, Vehicle-to-Network (V2N) services must operate under high mobility, dynamic traffic conditions, and multi-tenant environments, while remaining resilient to security threats and operational anomalies. In this paper, we propose a 5G-based architecture for connected vehicles that addresses these challenges by combining deterministic communication, secure resource coordination, and runtime monitoring mechanisms. To enhance communication predictability beyond best-effort transport, the architecture integrates Time-Sensitive Networking (TSN) within the 5G transport network. Secure and transparent coordination across multiple stakeholders is supported through Distributed Ledger Technology (DLT), mitigating risks associated with centralized control. The architecture further incorporates heterogeneous data collection to enable adaptive resource management, as well as Runtime Verification and an AI-based anomaly detection system to monitor system behavior and network traffic in real time. By jointly addressing determinism, security, and adaptability within a unified 5G architecture, this work contributes a comprehensive foundation for reliable and secure connected vehicle services.
Vision-language-model (VLM)-guided reinforcement learning (RL) has recently attracted significant attention for it, replacing brittle hand-crafted rewards with semantically grounded signals; however, deploying such simulation-trained policies on real vehicles remains a fundamental challenge, because they rely on simulator-native observations and simulator-coupled action semantics with no counterpart on physical hardware. We identify a general principle: the simulation-to-reality gap decomposes into two largely orthogonal axes, a sensing-and-dynamics domain gap and a task-and-geometry gap, the former closable without real-world policy training by re-projecting real perception and control onto the policy's training manifold. We formalize this as a transfer guarantee that bounds the deployment gap by three independently controllable error terms, and instantiate it as Sim2Real-AD, which combines a Geometric Observation Bridge, a Physics-Aware Action Mapping, a Two-Phase Progressive Training curriculum, and a Real-time Deployment Pipeline. As a proof of concept, a CARLA-trained VLM-guided RL policy is transferred zero-shot to a full-scale battery-electric Ford E-Transit van in Madison, WI, USA, and drives across car-following, obstacle-avoidance, and stop-sign scenarios using no real-world training data. To our knowledge, this is among the first zero-shot closed-loop deployments of a CARLA-trained VLM-guided RL policy on a full-scale real vehicle, and the decomposition offers a principled, broadly applicable route for moving simulation-trained, foundation-model-guided policies into the physical world, supporting energy-efficient intelligent driving on electrified transportation platforms. The demo video, code, and model checkpoint are available at: https://zilin-huang.github.io/Sim2Real-AD-website/.
Munawar Hasan, Apostol Vassilev, Edward Griffor, Thoshitha Gamage
The application of zero-knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy operation in decentralized environments. zk-SNARK is a powerful cryptographic tool that allows a party (the prover) to prove to another party (the verifier) that a statement about its own internal state is true, without revealing sensitive or proprietary data about that state. This paper proposes Hermes Seal: a zk-SNARK-based ZKP framework for enabling privacy-preserving, verifiable communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. The framework allows autonomous systems to generate cryptographic proofs of perception and decision-related computations without revealing proprietary models, sensor data, or internal system states, thereby supporting interoperability across heterogeneous autonomous systems. We present two real-world case studies implemented and empirically evaluated within our framework, demonstrating a step toward verifiable autonomous system information exchanges. The first demonstrates real-time proof generation and verification, achieving 8 ms proof generation and 1 ms verification on a GPU, while the second evaluates the performance of an autonomous vehicle perception stack, enabling proof of computation without exposing proprietary or confidential data. Furthermore, the framework can be integrated into AV perception stacks to facilitate verifiable interoperability and privacy-preserving cooperative perception. The demonstration code for this project is open source, available on Github.
Modern vehicles are distributed embedded computing platforms whose expanding network connectivity-CAN bus, Bluetooth, cellular telematics, and over-the-air (OTA) update channels-exposes them to the same class of adversarial attacks studied in cloud and enterprise environments. Machine learning (ML)-based intrusion detection systems (IDS) have emerged as the primary defensive response, yet these models are themselves vulnerable to adversarial perturbation: a well-crafted malicious CAN frame can evade an ML-based IDS in the same way that an adversarial image patch fools a computer-vision classifier. This paper traces the threat landscape from foundational automotive attack-surface studies through contemporary adversarial ML research, examines how resource-constrained embedded platforms limit defensive options, and proposes a defense architecture that combines behavioral anomaly detection with zero-knowledge proof (ZKP) attestation for invehicle control units. We argue that ZKP-based component attestation-previously dismissed as computationally impractical for embedded systems-is now feasible given recent advances in succinct non-interactive arguments of knowledge (SNARKs), and that combining it with adversarially trained ML-IDS models yields defensein-depth that addresses both network-layer and hardware-layer attack vectors.
Xingxing Chen, Xiaohong Zhang, Shaojiang Zhong, Shuling Liu
Vehicular Ad Hoc Networks (VANETs) are now a pivotal component of Intelligent Transportation Systems. However, ensuring secure vehicle identity authentication and protecting user privacy remain two challenging issues in VANETs. Addressing these challenges, this paper seamlessly integrates blockchain technology with the InterPlanetary File System to realize a fully decentralized storage solution for identity verification information. Simultaneously, it employs zk-SNARK and elliptic curve cryptography to allow vehicle users to anonymously complete identity verification. Additionally, the lightweight identity authentication proof obtained after successful verification maintains credibility while reducing the computational and communication costs for both roadside units and vehicles. The security and performance analysis of the system show that the proposed scheme has significant advantages in both communication and computation compared with similar research, while also offering superior security and a broader range of functional attributes compared to existing competitive approaches.
With the advancement of edge intelligence technology and the acceleration of urbanization, intelligent transportation systems (ITS) have experienced rapid development. Vehicle-road-cloud (VRC) collaboration was enabled through the coordinated sharing of vehicle-to-vehicle (V2V), vehicle-to-road (V2R), and vehicle-to-cloud (V2C) data in the Internet of vehicles, thereby constructing a more efficient cooperative intelligent transportation system (C-ITS). However, numerous security threats in VRC collaboration were found to severely impede the development of cooperative autonomous driving. The development status of VRC collaboration was first summarized, and the history of autonomous driving and the VRC-based autonomous driving environment were elaborated. Subsequently, attacks and security defense technologies in VRC collaboration were systematically categorized into two types: classical information security mechanisms and defense technologies, which were detailed from five aspects—information availability, integrity, confidentiality, authenticity, and non-repudiation; and machine learning-based security threats and defense technologies, which were analyzed from both centralized and distributed perspectives. Finally, future development directions and research priorities of VRC collaborative security technologies were forecasted, primarily covering federated learning, blockchain technology, secure multi-party computation, zero-knowledge proof, and differential privacy technology.
Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Xianjun Deng, Jong Hyuk Park
Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the remaining challenges for securing V2X communications in the face of evolving threats.
Merkle proof-based airdrops are a widely used mechanism in decentralized finance (DeFi) for distributing tokens securely and efficiently to eligible recipients. By leveraging Merkle trees, these airdrops minimize storage requirements and reduce transaction costs while ensuring the integrity of recipient lists. Unfortunately, their importance also attracts sophisticated attacks. Secondary preimage attacks, in particular, pose a major risk to the security of token distribution, potentially enabling adversaries to exploit vulnerabilities and drain tokens from airdrop contracts. This paper introduces FSA: a F ramework for analyzing S econdary preimage A ttacks on Merkle proof-based airdrops, designed to systematically address such vulnerabilities. The framework operates through four core contributions: (1) a validated quantitative damage assessment model using proof-of-concept experiments, (2) an analysis of attack repetition patterns in binary Merkle trees and Merkle Mountain Ranges, (3) identification of key factors influencing attack success, including token balances, hash values, and variable sizes, and (4) evaluation of mitigation strategies such as using non-64-byte leaf values, asymmetric hashing, and proof length validation. Through extensive analysis using the FSA, our experimental results demonstrate the framework's effectiveness in uncovering vulnerability patterns and developing targeted countermeasures.
The emerging paradigm of modern vehicles as sophisticated mobile data centers generates unprecedented volumes of telemetry, sensor, and interaction data that require novel management approaches. The architectural framework addresses dual requirements of edge processing for latency-sensitive applications and cloud infrastructure for deeper analytics and model development. Vehicle-to-everything communication protocols integrate with software-defined networks and distributed ledger technologies to ensure secure, efficient data exchange across the ecosystem. Technical challenges including bandwidth constraints, data redundancy, and privacy regulations are primary motivators for solutions based on federated learning, optimized compression algorithms, and context-aware processing. Resilient vehicular data management necessitates a multi-layered approach balancing computational requirements across the edge-cloud continuum while maintaining robust security postures. These foundations enable scaling next-generation intelligent transportation systems were vehicles function as key nodes in broader smart city infrastructures.
Vehicular Ad Hoc Networks (VANETs) are essential to intelligent transportation systems (ITS), enabling secure, real-time communication among vehicles and infrastructure. However, their decentralized and dynamic nature makes them vulnerable to threats such as Sybil attacks, message forgery, replay attacks, and Denial-of-Service (DoS). This paper presents VANETGuard, a lightweight scalable trust management system that enhances security and scalability in 5G-enabled smart vehicular networks. The proposed system integrates entropy-based anomaly detection, Bayesian inference for adaptive trust scoring, and a lightweight distributed ledger for decentralized, tamper-resistant trust storage. Large-scale simulations under realistic traffic and attack conditions demonstrate that VANETGuard achieves 99.97% detection accuracy, significantly reduces false positives, and maintains low latency and computational overhead while supporting over 300 vehicles. These results highlight VANETGuard’s potential to enable secure, efficient, and scalable trust mechanisms in next-generation ITS and urban mobility systems.
Zahraa Sh. Alzaidi, Ali A. Yassin, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi
Authentication of vehicles and users, integrity of exchanged messages, and privacy preservation are essential features in VANETs. VANETs are used to collect information on road conditions, vehicle location and speed, and traffic congestion data. The open exchange of information within VANETs poses serious security threats. Furthermore, existing schemes have higher communication and computational costs, making them incompatible with resource-constrained VANET applications. This study proposes a multifactor authentication and privacy-preserving security scheme for VANETs based on blockchain and fog computing to meet all these requirements. The proposed scheme uses fingerprints and Quick Response (QR) codes as a multifactor to authenticate vehicle users and fog-cloud computing techniques to reduce the computational burden on RSUs and improve service quality and resilience. Additionally, the scheme synchronizes a consistent ledger across all RSUs using blockchain technology to store and distribute vehicle authentication statuses. Through a thorough comparison with relevant current protocols, the scheme shows a much-reduced computing expense and communication burden in situations with high vehicle density within a timeframe of 6.3846 ms and 544 bytes for communication costs. In addition, the proposed scheme demonstrates a successful balance between efficacy and complexity, protecting confidentiality, anonymous authentication, and ensuring integrity and conditional tracking. Formal and informal security analysis showed that the proposed scheme is more reliable, practical, and secure against many hostile attacks, such as modification attacks, 51% attacks, Sybil attacks, and MITM attacks.
This study introduces a secure and scalable hybrid trust management system for Vehicular Ad Hoc Networks (VANETs), designed to detect and mitigate diverse malicious activities, including Sybil attacks, Denial of Service (DoS), and data forgery. Integrating entropy-based anomaly detection, Bayesian reputation updating, and the IOTA Tangle as a distributed ledger, the system ensures robust, real-time trust evaluation in dynamic vehicular environments. Rigorous testing against adversarial scenarios demonstrated high detection accuracy, low latency, and resilience under varied attack conditions. Key contributions include enhanced scalability through edge computing, secure reputation storage using Distributed Ledger Technology (DLT), and real-time anomaly detection.
Vehicular Ad hoc Network (VANET) is considered one of the feasible solutions to improve the efficiency and safety of modern transportation systems, and it provides new opportunities for creating a safe and efficient traffic environment. In recent years, this technology has attracted extensive attention from the academic community. However, VANET is an open network with frequent information interaction, and users are vulnerable to security and privacy threats. The existing schemes mainly consider the identity authentication of vehicles in vehicle enterprises (VEs). Due to concerns about the leakage of core data, VEs lack the motivation to establish a cross-vehicle enterprise identity authentication framework. Based on the above analysis, we propose a cross-vehicle enterprise authentication architecture by designing a two-stage certificate generation mechanism where certificate authority (CA) and VEs cooperate to generate identity credentials for vehicles. To address the concerns of VEs, we establish distributed trust and enable information sharing across VEs by introducing a consortium blockchain composed of car companies, CA, and pseudonym certificate authority (PCA). Considering the need for vehicles to access road traffic information, we use a public blockchain to store public information, and the practical byzantine fault tolerant (PBFT) algorithm is used to reach consensus. Instead of using computationally complex bilinear pairing and mapping-to-point hashing operations, the proposed scheme uses an elliptic curve cryptosystem (ECC), considering the limited hardware resources of the vehicle and RSU. In addition, our scheme integrates edge computing to solve complex computing tasks that cannot be performed locally and further reduces system latency. Security analysis and performance analysis show that our scheme has better performance than existing schemes in terms of security, computational overhead, and communication overhead.
The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. The proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.
Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.
Hugues Blache, Pierre-Antoine Laharotte, Nour‐Eddin El Faouzi
The deployment of Automated and Connected Vehicles (ACV) into traffic requires certifications and validations guaranteeing high levels of safety, security and reliability. The underlying objective is to gain public acceptance by proving that automation systems might bring out a safer mobility. While plenty of methods to certify these systems are populating the literature, the scenario-based approach stands out by reducing the quantity of required Field tests to validate any new system at stake. In this study, we refine the scenario-based approach by proposing a proof of concept (PoC) for scenario reduction using criticality metrics. For this PoC, we weave a relationship between the a priori criticality of abstract functional scenarios and the words used to generate them. Once, the criticality of a subset of scenarios is qualified based on open field data (HighD), the Latent Dirichlet Allocation (LDA) clustering approach is used to generate topics and feature the relationship between observed criticality and semantics words applied to functional scenarios. The criticality degree of semantics words is used to predict the a priori criticality of unobserved functional scenarios.
The wave of modernization around us has put the automotive industry on the brink of a paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily transitioning towards automated driving to constitute an integral part of the intelligent transportation system (ITS). The term autonomous vehicle has become ubiquitous in our lives, owing to the extensive research and development that frequently make headlines. Nonetheless, the flourishing of AVs hinges on many factors due to the extremely stringent demands for safety, security, and reliability. Cutting-edge technologies play critical roles in tackling complicated issues. Assimilating trailblazing technologies such as the Internet of Things (IoT), edge intelligence (EI), 5G, and Blockchain into the AV architecture will unlock the potential of an efficient and sustainable transportation system. This paper provides a comprehensive review of the state-of-the-art in the literature on the impact and implementation of the aforementioned technologies into AV architectures, along with the challenges faced by each of them. We also provide insights into the technological offshoots concerning their seamless integration to fulfill the requirements of AVs. Finally, the paper sheds light on future research directions and opportunities that will spur further developments. Exploring the integration of key enabling technologies in a single work will serve as a valuable reference for the community interested in the relevant issues surrounding AV research.
Vehicular Ad-hoc Network (VANET) is a modern concept of transportation that was formulated by extending Mobile Ad-hoc Networks (MANETs). VANET presents diverse opportunities to modernize transportation to enhance safety, security, and privacy. Direct communication raises various limitations, most importantly, the overhead ratio. The most prominent solution proposed is to divide these nodes into clusters. In this paper, we propose a clustering mechanism that provides security and maintains quality after the cluster formulation based on the pre-defined Quality-of-Service (QoS) parameters. To address potential attacks in the VANET environment, the proposed mechanism uses blockchain to encrypt the trust parameters’ computation. A particular trust degree of a vehicle is evaluated by the base station, encrypted with the blockchain approach, and transmitted toward roadside units (RSUs) for further utilization. The system’s performance is evaluated and compared with the existing approaches. The results show a significant improvement in terms of security and clustering quality.
Abstract Because of a large number of vehicles in Internet of Vehicle(IoV), distributed nodes and complex driving environment, data security and certification speed are easily affected. Blockchain enables different devices that do not trust each other to work together, maintain the general state in the process of information dissemination and sharing, and protect the privacy of devices. However, at present, the speed of vehicle certification in IoV is slow, and the use of idle resources is not considered. To address this problem, this paper provides a blockchain-based vehicle identity verification scheme by using a hybrid identity code verification method to ensure the nodes in the network securely share information. Meanwhile, a task processing algorithm based on time window is proposed to optimize the utilization of idle resources. In addition, the method is evaluated by simulation experiment, and the designed scheme can reduce malicious behavior of a registered vehicle in the network, and can shorten the processing task delay.
Sudan Jha, Nishant Jha, Deepak Prashar, Sultan Ahmad · 6 authors
Autonomous vehicles offer various advantages to both vehicle owners and automobile companies. However, despite the advantages, there are various risks associated with these vehicles. These vehicles interact with each other by forming a vehicular network, also known as VANET, in a centralized manner. This centralized network is vulnerable to cyber-attacks which can cause data loss, resulting in road accidents. Thus, to prevent the vehicular network from being attacked and to prevent the privacy of the data, key management is used. However, key management alone over a centralized network is not effective in ensuring data integrity in a vehicular network. To resolve this issue, various studies have introduced a blockchain-based approach and enabled key management over a decentralized network. This technique is also found effective in ensuring the privacy of all the stakeholders involved in a vehicular network. Furthermore, a blockchain-based key management system can also help in storing a large amount of data over a distributed network, which can encourage a faster exchange of information between vehicles in a network. However, there are certain limitations of blockchain technology that may affect the efficient working of autonomous vehicles. Most of the existing blockchain-based systems are implemented over Ethereum or Bitcoin. The transaction-processing capability of these blockchains is in the range of 5 to 20 transactions per second, whereas hashgraphs are capable of processing thousands of transactions per second as the data are processed exponentially. Furthermore, a hashgraph prevents the user from altering the order of the transactions being processed, and they do not need high computational powers to operate, which may help in reducing the overall cost of the system. Due to the advantages offered by a hashgraph, an advanced key management framework based on a hashgraph for secure communication between the vehicles is suggested in this paper. The framework is developed using the concept of Leaving of Vehicles based on a Logical Key Hierarchy (LKH) and Batch Rekeying. The system is tested and compared with other closely related systems on the basis of the transaction compilation time and change in traffic rates.
Through information sharing, vehicles can know the surrounding road condition information timely in Vehicular Adhoc Networks. To ensure the validity of these messages and the security of vehicles, the message authentication, privacy-preserving, and delay problems are three important issues. Although many conditional privacy-preserving authentication schemes have been proposed to ensure secure communication, there still exist some imperfections such as frequent interactions or unlinkability. From this, our paper proposes a novel hierarchical blockchain-assisted authentication scheme to solve these existing issues comprehensively. First, unlinkability is achieved by a dynamic key derivation algorithm. Second, the proposed scheme can reduce correlation processing delay, queuing delay, and deployment costs by adopting hierarchical Vehicle Fog Computing. Third, cross-region authentication is achieved by taking advantage of the properties of blockchain. In addition, we demonstrate our scheme can fulfill the security criteria of the Vehicular Adhoc Network by security analysis. Furthermore, the simulations are carried out to show its availability by using JAVA and NS-3. The findings reveal that the suggested method outperforms earlier schemes in terms of computation cost and communication cost. All in all, making the authentication scheme more efficient and concise is the focus of our future research.