The rapid evolution of the Internet of Vehicles (IoV) necessitates secure, scalable, and low-latency route navigation mechanisms that can operate in highly dynamic vehicular environments. Emerging paradigms such as Vehicular Digital Twins (VDTs) further enhance IoV ecosystems by enabling real-time virtual representations of physical vehicles, facilitating predictive analytics, intelligent decision-making, and context-aware routing. However, conventional VANET-based approaches suffer from centralized trust dependencies, high computational overhead, and limited adaptability to real-time traffic conditions. This paper proposes BFRN-IoV, a blockchain- and fog-enabled route navigation framework that integrates lightweight ECC-HMAC-based mutual authentication, RSU-assisted fog routing, and global route validation via a Geo-Location Provider (GLP), while leveraging VDTs for enhanced situational awareness and dynamic route optimization. The framework ensures key security properties-including confidentiality, integrity, pseudonymity, unlinkability, and non-repudiation-using ECDH-derived session keys, HKDF-based key expansion, and HMAC verification, while preserving privacy through pseudonym-based identity management. A permissioned blockchain provides immutable and auditable logging of routing interactions without exposing vehicle identities. Simulation results using SUMO and implementation via Web3 demonstrate significant improvements in routing accuracy, along with reduced communication and computational overhead compared to existing approaches. Formal verification using the Scyther tool confirms robustness against replay, impersonation, and man-in-the-middle attacks. The proposed framework delivers a unified, secure, and efficient solution for real-time IoV route navigation, further strengthened by the integration of VDTs in next-generation intelligent transportation systems.
C. Selvan, M. A. Gunavathie, Sini Anna Alex, Shaik Jaffar Hussain
ABSTRACT Appropriate routing strategies are necessary for mobile ad hoc networks (MANETs) in order to facilitate effective data transfer. In order to counter the prevailing problems, the correct routing schemes will need to be selected as the default configurations are used. In this paper, a special optimal link state routing (OLSR) protocol is proposed to incorporate a deep learning methodology to facilitate efficient video streaming in MANETs. This study presents a new improved variant of the OLSR protocol, which is specially tailored to achieve efficient video streaming in MANETs. It is a radical approach that combines a deep‐learning model with blockchain technology to overcome security and reliability issues. It starts with the gathering of video content that is available publicly. In order to detect black‐hole nodes, a special twin‐attention‐based Elman spiking neural network model is applied. The reliability of the neighboring nodes is then measured by means of trust values. The pufferfish optimization algorithm, or the accuracy‐aware energy‐efficient multipath routing algorithm (AEMRAP), which takes into account node‐ and link‐stability degrees, is used in making routing decisions. Interplanetary file system (IPFS) technology is used to store the data on blockchain and increase its security. The authentication of the blockchain architecture is conducted via the delegated proof‐of‐stake (DPoS) method that also delivers an extra protection of MANETs against unauthorized access. The study demonstrates superior performance in securing and optimizing video transmission, confirming that the extended OLSR protocol is highly effective for MANET video streaming applications. The proposed model exceeds the current approaches with a throughput of 2100 Kbps, an average end latency of 20.2 s, and a packet‐delivery ratio of 92.3%.
Vehicular ad-hoc networks (VANETs) play a vital role in enhancing modern transportation systems, facilitating real-time data exchange in dynamic environments. However, VANETs face challenges such as limited range and interference in dense areas. This paper introduces an unmanned aerial vehicle (UAV)-aided reputation-based cluster routing (URCR) protocol to address issues such as improving data transmission, reducing delay, and lowering hop count. The proposed URCR protocol utilizes VANET clustering, UAV-aided communication, and a Proof-of-Stake based cluster head-toggling algorithm to achieve balanced energy consumption. Blockchain-based reputation management is integrated into the protocol to evaluate the trustworthiness of the member nodes and prevent malicious behavior in the VANET. Simulations using Network Simulator 3 show that URCR improves the average hop count by 47.6% and 15.2%, the packet delivery ratio by 22.9% and 4.3%, and the end-to-end delay by 48.8% and 22.3%, compared to drone-assisted cooperative routing (DACR) and VANET routing with UAV assistance (VRU), respectively.
High-mobility Internet of Things (IoT) for Vehicle-to-Grid (V2G) Demand Response (DR), including roaming between Charge Point Operators (CPOs), requires privacy-preserving authentication with sub-1 ms responses and cross-domain scalability as devices exceed 200km/h. Mechanisms must run on constrained hardware while remaining compatible with EV-charging message flows such as ISO 15118–20 and OCPP 2.0.1. Many deployed schemes re-authenticate from scratch, which inflates computation and airtime; static credentials also ignore trajectory context and struggle with rapid mobility. We present a Zero-Knowledge Proof-based Authentication Scheme (ZKPAS) for V2G/DR that proves possession without disclosure and replaces heavy handshakes with compact, mobility-aware proofs, targeting latencyLO(n) toO(logn). (iii) Predictive token generation with Long Short-Term Memory (LSTM) models trained on GeoLife and T-Drive pre-computes material, yielding 84.7% token reuse along trajectories. (iv) Cross-domain authentication employs (t,n)-threshold cryptography for Byzantine-tolerant roaming across operators. We prove resistance to impersonation, replay, man-in-the-middle, and trajectory inference; under the Computational Diffie–Hellman Problem (CDHP), the adversary’s success probability satisfies Pr[break] ≤ 2−λ. On real transportation topologies, ZKPAS cuts computation by 71.8%, authentication latency by 93.9%, and energy by 69.5%, while interfacing with V2G/DR control flows. The protocol sustains a 98.5% authentication success rate at 250km/h.
Zia Ullah, Zia Ullah, Sanam Shahla Rizvi, Ibrar Ali Shah · 5 authors
Vehicular Ad Hoc Networks (VANETs) are essential for the success of Intelligent Transportation Systems (ITS), providing real-time communication between vehicles and infrastructure. However, the highly dynamic and decentralized nature of VANETs introduces significant challenges in ensuring trust and security across the network, including security threats, communication overhead, and energy inefficiencies. This paper presents a novel blockchain-based trust management framework that addresses these issues by incorporating lightweight consensus mechanisms, optimized data propagation strategies, and energy-aware protocols. Our approach reduces communication overhead by selectively propagating trust updates, leading to a 35% decrease in overall network traffic compared to traditional broadcast-based systems. In terms of trust accuracy, our model achieves over 95% accuracy in detecting malicious nodes, significantly outperforming existing solutions. The proposed system demonstrates the identification and penalization of malicious behaviors such as Sybil attacks and false reporting with a 25% improvement in detection rate, while maintaining low latency (an average reduction of 30% compared to PoW-based systems) and efficient energy consumption, reducing energy use by up to 40%. The proposed model also incorporates a hybrid Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which further enhances its scalability and fault tolerance. Simulation results show that our framework converges to accurate trust values faster than traditional methods, ensuring that reliable trust evaluations are made in real-time, even under high mobility conditions. The combination of these optimizations ensures that our framework is not only secure but also highly efficient, capable of supporting scalable and resilient VANET deployments. Furthermore, our decentralized approach ensures that trust decisions are made in real-time without the need for a centralized authority, making the system more adaptable to the high-mobility conditions of VANETs. This research offers a comprehensive solution for VANETs trust management, significantly improving communication efficiency, trust accuracy, and energy consumption while maintaining robust security and scalability. Our proposed blockchain-based trust management system provides a secure, energy-efficient, and scalable solution for VANETs, setting the stage for future developments in secure vehicular communication networks.
As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold t of n across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.
This interdependent network of vehicles has been made possible by the widespread adoption of self-driving cars operating as interconnected swarm networks based on continuous data exchange. Leading a Data-Driven Paradigm Shift However, these networks are not without their challenges, as they are prone to security threats, data privacy vulnerabilities, and inefficient consensus mechanisms to facilitate decentralized decision-making. This study proposes a novel blockchain architecture using AI that could offer enhanced security by utilizing a hybridized consensus protocol for autonomous vehicles. Accordingly, machine learning algorithm-based optimizations in blockchain consensus algorithms, such as PoW, PoS, and DPoS consensus methods, facilitate real-time adaptive adjustments, lower mining costs, and accelerated transaction validation, all within the framework of decentralized trustworthiness. In self-driving car ecosystems, AI-augmented security processes like anomaly detection, deep learning-based intrusion prevention, and federated learning, enhance threat detection while lowering the cybersecurity risks. Moreover, privacy-enhancing cryptographic methods, such as homomorphic encryption, zero-knowledge proofs (ZKPs), and differential privacy, are incorporated to safeguard sensitive vehicle information against unauthorized access while allowing compliance with data privacy laws. Experimental evaluations confirm that the proposed AI-empowered frameworks lead to improved system resilience, optimized resource allocation and improved transaction throughput and latency in contrast to traditional blockchain implementations. Overall, this study demonstrates that using AI-enabled blockchain models can provide a fundamental method for protecting and improving autonomous vehicle networks.
The Internet of Vehicles (IoV) is rapidly evolving to provide high throughput, low bandwidth, and enhanced security for high-quality information transfer with minimal latency. However, before effectively adopting all these services, IoV faces various hurdles, including secure information sharing, tampering with information, and network overhead. IoV with blockchain provides substantial advantages for secure information sharing, tamper-proof storage of information, and efficient network administration. We propose EtherChain, a blockchain-based secure approach for IoV to provide information integrity, tamperproof distributed storage, and efficient network administration. EtherChain uses transaction validation, trust, and miner selection algorithms to verify transactions, manage trust between nodes, and select the miner to append the block to the blockchain. Following that, EtherChain compares security strength in terms of several forms of security attacks with other cutting-edge techniques, as well as performance outcomes in terms of throughput, cryptography operation time, and communication overhead. The research demonstrates that EtherChain outperforms state-of-the-art solutions in terms of performance and security.
Rafael Abreu, Alexandre Valente Sousa, Luís Correia, Arsénio Reis · 7 authors
The integration of Artificial Intelligence (AI), Internet of Things (IoT), and Vehicle-to-Everything (V2X) technologies in green mobility systems introduces new cybersecurity and privacy challenges. This paper proposes a lightweight cybersecurity framework that integrates compact convolutional neural networks (CNNs) for real-time anomaly detection at the edge, federated learning for decentralized model training, and blockchain-based decentralized identity management with zero-knowledge proofs. These mechanisms collectively ensure sub-100 ms threat detection latency, reduced communication overhead, and GDPR-compliant privacy preservation. Simulation results demonstrate a 60% reduction in latency, 45% lower communication costs, 30% energy savings at edge nodes, and a detection accuracy of 93.4% compared to traditional cloud-centric models.
Vehicular Ad Hoc Networks (VANETs) are emerging as a cornerstone of intelligent transportation systems, enabling efficient data exchange between vehicles and roadside infrastructure. However, ensuring secure and trustworthy routing remains a major challenge due to high node mobility, intermittent connectivity, and malicious node behavior. This research proposes a Blockchain-Assisted Trust-Driven Secure Routing Protocol (BTDSRP) to enhance communication reliability, data integrity, and routing efficiency in VANETs. The proposed BTDSRP integrates blockchain technology with a hybrid trust evaluation model to ensure transparent and tamper-proof routing decisions. Each vehicle maintains both direct trusts, derived from historical packet forwarding behavior, and indirect trust, computed from neighboring recommendations. These trust values are recorded in a lightweight blockchain maintained through a Delegated Proof of Stake-Byzantine Fault Tolerant consensus mechanism, ensuring decentralized verification with minimal latency. Routing decisions are optimized using a Multi-Objective Optimization Function that jointly considers trust, link stability, delay, and residual energy to identify the most reliable path for data forwarding. A Trust Update Algorithm dynamically adjusts node reputation based on deviation from expected behavior, thereby isolating malicious nodes in real time. Extensive simulations using NS-3 and SUMO demonstrate that BTDSRP achieves superior performance compared to the conventional protocols such as AODV, DSR, and TRUSTAODV, yielding improvements of up to 18.7% in packet delivery ratio, 23.4% reduction in end-to-end delay, and 15.2% decrease in routing overhead. The results confirm that integrating blockchain with trust-based routing significantly enhances the security, transparency, and efficiency of vehicular communication networks, providing a robust foundation for next-generation 5G/6G-enabled intelligent transportation systems.
Internet of vehicles (IoV) achieves this through the provision of easy and real time communication between vehicles, roadside infrastructure, and cloud services. However, its dynamic and heterogeneous environment presents significant authentication challenges, with conventional Public Key Infrastructure (PKI) approaches often proving unscalable, slow, and dependent on centralized authorities. This paper presents a blockchain-based authentication framework that leverages Decentralized Identity (DID), cryptographic hashing, digital signatures, and smart contracts to address these limitations. In the proposed system, each vehicle generates a unique DID, signs event data using its private key, and records authentication proofs immutably on the blockchain. The DID and the reputation that goes along with it allow verifiers to ascertain data integrity and authenticity without depending on centralized trust entities. By removing single points of failure and ensuring resistance to impersonation and data tampering, this approach delivers a low-latency, scalable, and secure authentication mechanism tailored for next-generation vehicular networks.
The union of Blockchain and Federated Learning (FL) technologies in the Internet of Vehicles (IoV) domain has ushered in new possibilities for privacy-preserving, decentralized, and cyber-resilient financial use cases. As autonomous and connected vehicles (CAVs) are integrated with edge computing and 5G/6G networks, these smart nodes increasingly engage in real-time financial transactions spanning from usage-based insurance, automated tolls, electric vehicle charging fee payments, to intelligent vehicle leasing and decentralized vehicular identity management. Such a dynamic scenario is best addressed by Blockchain technology, which provides a decentralized ledger system that promises immutability, transparency, and trust, which are perfect for authenticating transactions like insurance claims, micropayments, and contractual fulfilment in mobility finance. This enables IoV stakeholders to build predictive systems with data sovereignty and low latency. Even so, the joint employment of blockchain and FL in financial environments poses important regulatory and ethical challenges.
Message spoofing and denial-of-service (DoS) attacks threaten vehicular network security by disrupting communication channels and falsifying safety-critical data. Traditional intrusion detection systems (IDS) exhibit high computational overhead and limited adaptability to evolving attack patterns. This paper presents a hybrid security framework integrating Language Agent Models (LAM) with a dual-layer blockchain architecture for real-time threat detection in Internet of Vehicles (IoV) networks. The LAM operates on edge devices to analyze heterogeneous data streams from CAN bus, V2X, and GPS sources. It identifies spoofing and DoS anomalies through transformer-based attention mechanisms with fewer than 1 billion parameters. The dual-layer blockchain combines Proof-of-Authority-and-Association (PoA2) consensus at layer 1 with zero-knowledge rollup (zk-Rollup) at layer 2. This architecture ensures tamper-proof alert logging while reducing on-chain storage overhead. The PoA2mechanism employs pre-authenticated validators to achieve microsecond-scale transaction finality. The zk-Rollup layer aggregates alert transactions into cryptographic validity proofs, minimizing blockchain storage requirements. Performance evaluation demonstrates the framework’s effectiveness across multiple metrics. Detection accuracy reaches 95.7% on the CICIoV2024 dataset and 96.9% on the Car-Hacking dataset. Precision exceeds 97% with F1-scores above 95% on both benchmarks. The system maintains false positive rates below 5.2%. End-to-end response latency remains under 5 milliseconds (ms), meeting real-time safety requirements. The blockchain layer processes over 2,187 transactions per second with 25 validator nodes. Storage optimization achieves a 92% reduction in on-chain data volume. Energy consumption decreases by 4.3 times compared to cloud-hosted language models. The proposed architecture provides deterministic threat detection with cryptographic auditability for large-scale IoV deployments.
Vehicle platoon (VP), as a typical form of traffic cooperation, can significantly enhance traffic efficiency and safety in Vehicular Ad hoc Networks (VANETs). However, malicious vehicles in VP poses a severe threat to the security of entire VP, requiring to be efficiently traced by identity authentication. In this paper, we propose a lattice-based efficient and traceable privacy-preserving batch authentication scheme for vehicle platoon in VANETs, named LETA. First, we design a dynamic VP identity structure VPD-Tree which is constructed based on hash tree and pseudonyms of vehicles to preserve privacy. Then, an aggregate signature is constructed based on VPD-tree and modular lattice for secure and efficient batch authentication of VP. Finally, Zero-Knowledge Proofs (ZKP) is applied on the VPD-Tree structure to anonymously and efficiently trace the malicious vehicles of VP. Security analysis shows that LETA achieves stronger security guarantees, thereby offering a more secure solution than existing approaches. Moreover, performance evaluations show that LETA achieves lower computation and communication overheads through the VPD-tree structure and efficient batch authentication scheme.
In the need for high-security mechanisms, Trust Management Systems (TMSs) are implemented in vehicular networks such as Vehicular Ad Hoc Networks (VANETs) and the Internet of Vehicles (IoV) to ensure reliable interactions between vehicles. These systems nowadays are a key factor in building up security by evaluating and managing trust relationships among network participants. However, TMSs are inherently very vulnerable to Trust Manipulation Attacks (TMA), where we find malicious nodes attempting to deceive trust models by exploiting their evaluation mechanisms. One critical variant of this attack involves malicious nodes creating multiple fake identities, known as the Sybil attack, to falsely reinforce their trustworthiness. This deception will totally mislead legitimate vehicles, manipulate the decision-making processes, and at the end compromise the overall security and reliability of the network. To address this challenge, we propose a Zero-Knowledge Proof (ZKP)-based trust authentication scheme that ensures each vehicle can prove its legitimacy without exposing sensitive information. Our approach can and will prevent attackers from fabricating multiple identities to manipulate trust values. By integrating cryptographic authentication with trust management, our method significantly strengthens security and ensures that only legitimate vehicles can participate in trust-based evaluations. Through simulations, we demonstrate the effectiveness of our proposed solution in reducing the risk of Sybil-based. The results indicate that our approach not only enhances security but also maintains efficient trust computation, making it a viable solution for real-world vehicular networks.
Chandni Patel, Parth Sheth, Megh H. Shah, Dev Mehta · 8 authors
One of the main issues with the Industrial Internet of Things (IIoT) in V2X communication is the threat of attacks. A comprehensive Intrusion Detection System (IDS) and a transparent ledger are important for providing an Intelligent Transportation System (ITS) beyond 5G. However, another major problem is that it is centralized and lacks a clear explanation of traditional IDS. By integrating Federated Learning (FL) to make it distributed and Explainable AI (XAI) to add a brain to the black box model to add the explanation factor, we make the model more robust and suitable for real-life situations. In this approach, we experimented using the X-IIOTID dataset. This dataset is a real-time indicator of the attack in an IIoT network such as V2X. It provides difficult and real-time scenarios that highlight the complexity of IDS. Furthermore, the benign data the model classifies is stored in the blockchain to make the system secure and transparent. Our FL-XAI-based technique provides an accuracy of $98 \%$ results than previous models. The proposed approach provides a clear and brief view of factors that affect classification actions, which helps users make security decisions. Evaluation of Pravah based on latency, accuracy, precision, recall, F1-score, and ROC-AUC confirms its effectiveness. This study contributes towards a more secure and interpretable ITS, bridging the gap between model performance and real-world applicability.
Vehicular ad-hoc networks (VANETs), considered a pivotal component of intelligent transportation systems (ITS), are susceptible to both established and emerging security vulnerabilities. However, existing authenticated key management schemes fail to provide effective conditional anonymity during decentralized authentication process. Meanwhile, scalable and reliable vehicular pseudonym management is absent, resulting in potential privacy leakage. Furthermore, conventional group key agreement schemes inherently fail to properly accommodate the highly dynamic topological characteristics of vehicular environments, which significantly limits their practical applicability. To address these challenges, the blockchain-assisted anonymous authentication and tree-based group key agreement design is proposed in this paper. Firstly, the pairing-free decentralized authentication mechanism is designed to enable mutual authentication between vehicles and roadside units (RSUs). Secondly, the threshold-varying pseudonym management system is designed, leveraging secret sharing and smart contracts to ensure conditional privacy preservation. This mechanism utilizes the multi-RSU consensus to recover the user's real identity, enabling traceability of malicious entities. Thirdly, the self-balancing tree-based group key agreement mechanism is proposed, optimizing key generation efficiency in dynamic vehicular environments. Crucial security requirements can be satisfied via the security analysis, whereas the performance evaluation substantiates the superiority of the proposed scheme over existing approaches.
Intrusion Detection Systems (IDS) play a critical role in protecting modern networks, but traditional centralized designs raise serious concerns regarding data privacy, trust, and scalability. Federated Learning (FL) reduces privacy risks through decentralized model training, and blockchain enhances trust by providing immutability and transparency. Combining these technologies creates a promising paradigm for secure and trustworthy IDS. This paper presents a comprehensive survey of blockchain-federated IDS with a particular focus on privacy and trust. The key contribution is a multi-dimensional taxonomy that integrates IDS architectures, FL strategies, blockchain types, and consensus mechanisms, providing a clear and structured view of this emerging field. We categorize threats into data, communication, and model levels, and map representative defense mechanisms to each. We also review applications in vehicular networks, industrial and medical Internet of Things (IoT), and metaverse scenarios. Finally, we highlight key challenges, including non-IID data, lightweight consensus, incentive mechanisms, and poisoning-resilient aggregation, and outline future research directions.
Ping Wang, Fei Tang, Ankui Jing, Lei Liu · 6 authors
In vehicular ad-hoc networks (VANET), federated learning enables vehicles to collaboratively train global models for intelligent transportation without sharing raw data. However, global model training faces various potential risks, such as identity leakage, privacy inference, and malicious attacks, due to the dynamic network structure and untrusted wireless communication of VANET. To address these issues, a robust authentication mechanism for federated learning must be achieved to ensure the trustworthiness of model parameters. In this paper, we propose an efficient and privacy-preserving authentication scheme with lightweight encryption for federated learning in VANET using self-sovereign identity (SSI), called Fed-EALE. Fed-EALE constructs Merkle pseudonym identity trees with the aid of decentralized identifiers. Vehicle participants use unlinkable pseudonyms to achieve privacy protection. Fed-EALE utilizes verifiable credentials and zero-knowledge proof to build the authentication protocol to ensure the authenticity and integrity of model parameters from anonymous vehicles. In addition, to accurately identify and eliminate malicious participants in anonymous communications, Fed-EALE can track and recover the real identities of malicious vehicles. We perform a security analysis of Fed-EALE. Performance evaluations indicate that Fed-EALE reduces authentication overhead by approximately 76% compared to state-of-the-art protocols, while maintaining high stability and scalability in VANET.
Douglas L. L. Moura, Andre L. L. Aquino, Antonio A. F. Loureiro
The integration of multiple distributed ledgers in Intelligent Transportation Systems (ITS) introduces challenges for scalable and interoperable authentication. Traditional schemes, which rely heavily on Public Key Infrastructure (PKI), face limitations related to certificate management and key escrow. To address these issues, we propose a federated authentication system based on certificateless public key cryptography (CL-PKC) to enable seamless cross-domain and cross-chain authentication without relying on traditional certificates. The proposed approach is designed to operate at the edge, where authentication is performed close to the user to reduce latency and support mobility. Leveraging the CL-PKC scheme, each user independently generates and manages their own cryptographic keys. Simulation results show reduced credential generation time, lower network usage, and improved latency under heavy and cross-domain conditions.
Xiangyun Tang, Lidu Lou, Rui Peng, Tao Zhang · 9 authors
Vehicle Edge Computing (VEC) has emerged as a crucial element in modern vehicular computing systems, enhancing data processing efficiency between vehicles and nearby infrastructure, reducing latency, and improving the overall performance of intelligent transportation systems. However, VEC faces challenges, such as data inequality and privacy concerns, which may impede accurate data processing and decision-making across various components (e.g., vehicles, traffic signals, and roadside units). Existing studies attempt to address these challenges by relying on centralized servers to process cross-vehicle data. However, this approach introduces vulnerabilities, including single points of failure and potential performance bottlenecks. Moreover, many current methods overlook the need for data verifiability alongside privacy and security, thus complicating the traceability of data sources in vehicular environments. In this paper, we propose a verifiable, privacy-preserving cross-vehicle protocol based on relay chains, utilizing blockchain's distributed ledger technology to facilitate transparent and secure information sharing among vehicle edge nodes. Through tamper-proof bookkeeping and automated smart contracts, the protocol significantly enhances the efficiency and security of VEC. The relay chain functions as the central framework, employing homomorphic encryption and distributed private key technology to enable confidential data sharing and verifiable access to business-critical information across nodes. This solution effectively tackles the pressing challenges of privacy protection, reliability, and data traceability within current VEC systems. To enhance practicality, the protocol adopts a non-iterative and lightweight design, enabling efficient data exchange and low-latency cross-chain interaction in heterogeneous VEC systems. We demonstrate the feasibility and effectiveness of our protocol through extensive experimental data supported by theoretical analysis. The results show that the proposed protocol achieves competitive performance in computation cost, encryption latency, and cross-chain throughput, especially under increasing key sizes and node densities, confirming its efficiency and scalability in real-world vehicular deployments.