The presence of shared micro-vehicles, such as bicycles and e-scooters, has become increasingly common in modern urban environments, enhancing citizensâ access to public transportation by providing an efficient solution to the last-mile problem. In recent years, shared mobility has expanded to include larger vehicles, such as cars and sea vessels, facilitating transportation over longer distances and offering an alternative to private and public modes of transport. However, the seamless integration of these different transportation modes remains a significant challenge, as each type of vehicle has its own advantages and limitations. Furthermore, these transport services are often operated by different organizations that use distinct platforms and ticketing systems, further complicating coordination among them. In this work, we present the proposed approach and the developed system designed to facilitate the adoption and integration of different types of vehicles using AI and blockchain technologies. The system enables users to identify and utilize the most appropriate means of transport through a unified, blockchain-based mechanism. Preliminary evaluation results, based on simulated data, indicate that the system can significantly benefit citizens in a smart city environment and, when combined with appropriate investments in urban infrastructure, can substantially improve daily mobility.
Sathwik Narkedimilli, Tejas Sathish, Mounira Msahli, Abdul Wahid
Blockchain technology has emerged as a promising enabler for the Internet of Vehicles (IoV). It offers decentralized coordination, immutable data sharing, programmable smart contract logic, and adaptive consensus mechanisms to meet stringent vehicular requirements. This comprehensive review reviews the state-of-the-art blockchain-IoV systems from 2019 to 2025, systematically classifying them into five dimensions: architectural models & smart contracts, consensus & scalability, security & privacy, federated learning & decentralized AI, and data dissemination with digital twin integration. We analyze lightweight consensus variants (e.g., PBFT extensions, DAG and sharding designs) that achieve millisecond-scale latencies and thousand-transactions-per-second throughput, as well as cryptographic frameworks (ring/group signatures, zero-knowledge proofs, TEEs) that preserve anonymity and secure key material. We highlight anchored-on-chain federated learning workflows to incentivize collaborative model training under non-IID data, 5 G/6G-enabled digital twins for provenance-aware simulation, and massive heterogeneity in edge-cloud architectures. Our comparative evaluation underscores advantages including resilience to Byzantine faults, privacy-preserving data exchange, energy-efficient consensus, and scalable deployments. Finally, we identify open challenges, including dynamic consensus tuning, cross-domain interoperability, real-world testbeds, and postquantum resilience, and outline a research roadmap toward robust, production-grade blockchain-enabled IoV ecosystems.
Smart mobility services generate large volumes of sensitive location and identity data, raising critical concerns related to privacy leakage, âsecurity vulnerabilities, and trust in large-scale urban deployments. To address these challenges, this paper proposes a blockchain-based âprivacy-preserving framework for smart mobility services that integrates geo-indistinguishability, pseudonymous authentication, Zero-âKnowledge Proofs (ZKPs), and Proof-of-Authority (PoA) consensus into a unified architecture. The framework ensures end-to-end privacy âby combining calibrated location obfuscation with decentralized transaction validation and immutable auditability, thereby mitigating both âinference-based attacks and reliance on centralized trust.â The proposed framework was evaluated using the TAPAS Cologne mobility dataset, comprising 1,000 simulated vehicles and 20 block-âchain validators. Experimental results demonstrate that adversarial inference accuracy is reduced to below 12%, while approximately 75% ânavigation utility is preserved at balanced privacy budgets. Security analysis confirms robust protection against tracking, replay, Sybil, and âcollusion attacks, with replay attack success rates reduced from 70% to 2% through the enforcement of timestamps and nonces, along with âcryptographic verification.â Performance evaluation demonstrates that the framework achieves high throughput (1,200 transactions per second) with sub-second latency ââ(0.8 seconds) under realistic transaction loads. Storage growth is optimized to 2.1 GB per million transactions, and the PoA consensus âmechanism achieves approximately 30% lower energy consumption compared to Proof-of-Stake-based designs. In addition, resilience ex-âperiments confirm Byzantine fault tolerance under up to 30% malicious validator participation, without service degradation.â Overall, the results demonstrate the practical feasibility of deploying the proposed framework in real-world smart mobility ecosystems that ârequire simultaneous privacy preservation, scalability, and energy efficiency. The framework represents a significant step toward trustwor-âthy, privacy-aware, and sustainable smart-city mobility infrastructure, providing a robust foundation for next-generation decentralized mo-âbility servicesâ.
Vehicle-to-Everything (V2X) communication is at the center of autonomous mobility, as it enables vehicles to exchange real-time information with infrastructure, other vehicles on the road, including pedestrians, and the network. V2X has the potential to improve navigation, traffic flow, and safety but also brings with it the associated governance risks of data integrity, privacy, and cybersecurity. These risks are amplified by the interconnected nature of V2X networks, which handle sensitive data like vehicle locations and driver identities, necessitating robust security solutions. This systematic review considers blockchain as a remedy, with its decentralized architecture, cryptographic security protocols, and automation through smart contracts. A review of peer-reviewed literature from 2018 to 2025 highlights blockchain's role in tamper-proof communication, privacy preservation through Zero-Knowledge Proofs and Ring Signatures, and secure transaction automation. For instance, blockchain ensures data immutability by distributing trust across nodes, reducing vulnerabilities like data spoofing, while smart contracts streamline processes like toll payments. Challenges such as scalability, latency, and regulation are addressed with proposals such as 5G, edge computing, and governance frameworks. Hybrid blockchain systems, either public or private are examined in this study to obtain a trade-off between scalability and security in V2X networks. In addition, interoperability is proposed to facilitate the seamless exchange of data between V2X systems. Emerging consensus mechanisms like proof of stake and directed acyclic graphs are proposed to enhance scalability, while federated learning integrations bolster privacy. Pilot projects, such as those in Dubai and Singapore, demonstrate blockchainâs practical efficacy in securing V2X ecosystems. The review positions blockchain as a leading enabler of secure, efficient, and privacy-aware intelligent transport systems. Future research should focus on standardizing governance and addressing latency to ensure global adoption.
The convergence of vehicular technology, artificial intelligence (AI), and distributed computing has catalyzed the emergence of the Internet of Vehicles (IoV) as a cornerstone of next-generation intelligent transportation systems (ITS). By enabling vehicle-to-everything (V2X) communication, IoV supports cooperative perception, real-time decision-making, and autonomous driving. However, the reliance on large-scale, data-driven intelligence in IoV exposes systems to critical challenges, including adversarial poisoning, privacy leakage, identity forgery, and the fragility of centralized learning architectures. Federated Learning (FL) has been proposed as a promising paradigm to alleviate some of these issues by enabling distributed model training without centralizing sensitive vehicular data. Nonetheless, conventional FL remains vulnerable to security and trust limitations, particularly in dynamic vehicular environments. This thesis addresses these challenges by designing secure, privacy-preserving, and scalable FL frameworks that leverage distributed ledger technologies and cutting-edge security mechanisms.The thesis advances knowledge through four interconnected contributions. First, two novel optimization-driven poisoning attack models are introduced: PA-PSOSA and PAPSOGA, which combine particle swarm optimization with simulated annealing and genetic algorithms, respectively. These models demonstrate that even a small poisoning budget can substantially degrade global model utility under black-box and clean-label constraints, highlighting the urgency of robust defenses in vehicular FL. Second, a permissioned blockchain-enabled FL (BCFL) framework is proposed, in which consortium edge nodes running Practical Byzantine Fault Tolerance (PBFT) consensus replace the central aggregator. With blockchain integration and data validation mechanisms, this design ensures identity authentication, verifiable audit trails, and improved resilience against poisoning and Sybil attacks, while maintaining high model accuracy under adversarial conditions. Third, the framework is further enhanced to achieve inference-resistance by integrating secure aggregation (SecAgg) and differential privacy (DP), and lightweight with off-chain commitments. This design significantly reduces ledger storage requirements, increases system throughput, and mitigates inference-based privacy risks. Finally, to overcome the scalability limitations of PBFT-based BCFL, a DAG-enabled FL (DFL) framework is developed. By leveraging parallel validation, utility-score-based tip selection, and reputation-weighted aggregation, this framework significantly improves scalability, reduces communication complexity, and enhances robustness in asynchronous vehicular environments.Together, these contributions articulate a coherent progression from exposing vulnerabilities in vehicular FL to constructing secure, privacy-preserving, and scalable frameworks tailored for IoV ecosystems. The findings demonstrate that interdisciplinary integration of optimization theory, cryptography, differential privacy, and distributed ledger technologies is indispensable for trustworthy vehicular intelligence. Beyond theoretical significance, the proposed frameworks offer practical designs for deployment in safety-critical IoV environments. Future research directions include the integration of zero-knowledge proofs (ZKP) for verifiable privacy, adaptive defenses against evolving adversarial strategies, and experimental validation in real-world vehicular testbeds. Collectively, this thesis establishes a foundation for secure federated intelligence in IoV, contributing to the reliability, efficiency, and trustworthiness of next-generation ITS.
The application of Distributed Ledger Technology in Intelligent Transportation Systems ensures a secure and decentralized mechanism for data sharing among vehicles and infrastructure. However, DAG based protocols such as the IOTA Tangle when applied to autonomous vehicular systems face significant challenges in ledger consistency, transaction confirmation, and convergence due to the highly dynamic and intermittently connected nature of vehicular networks. To address these limitations, this paper proposes a Connectivity Aware Intelligent Distributed Ledger Construction Model tailored for autonomous vehicular environments. The proposed framework integrates a connectivity aware tip selection mechanism with a reinforcement learning strategy based on sliding window bias thompson sampling to dynamically select optimal ledger construction actions under non-stationary network conditions. Vehicular connectivity is modeled using an alternating renewal process, and transaction arrivals by a nonstationary Poisson process. Extensive simulation results demonstrate that CA-IDLCM outperforms URTS, MCMC, and Biased-TS approaches by achieving confirmation rate of 95.8%, and maintaining the orphan fraction well below 2%, with tip counts stabilizing near 1.0. highlighting its robustness, adaptability, and suitability for next generation Intelligent Transportation Systems.
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.
Smart transportation networks have the potential to significantly improve traffic flow. The Internet of Vehicles (IoV) serves as a vital component of such networks, enabling real-time connectivity and coordination among vehicles and infrastructure. The expansion of IoV-based communication and the increasing volume of data transferred across the IoV make it necessary to implement effective techniques for preserving privacy and ensuring information security. Nonetheless, traditional data-security models have notable drawbacks, primarily high computational costs. In our pseudonymous authentication framework, each vehicle first generates a publicâprivate key pair using a multidimensional lattice-based (Nth-degree truncated polynomial ring units) method. A vehicle then digitally signs its own identity with its private key and sends an authentication request to the roadside unit (RSU); the RSU then verifies that signature using the public key of the corresponding vehicle. After verification, the vehicle and RSU execute a secure ephemeral-key agreement using ephemeral supersingular isogeny DiffieâHellman to establish a shared session key. The complete authentication and session-key-agreement process is securely signed and documented on the blockchain using a lightweight enhanced delegated proof-of-stake consensus methodology to efficiently confirm the transaction and add it to the blockchain. The experimental findings show that the proposed system incurs a computational cost of 12.1 ms and a communication cost of 1184 bits. Furthermore, smart contracts are deployed on the Remix virtual machine to showcase the functionality of the proposed system within a decentralized blockchain environment. The smart contract execution costs are (681,713), (734,851), and (870,301) for the RSU registry, vehicle registry, and session AuthTrust, respectively. The proposed scheme is comparatively evaluated against existing frameworks, namely PBSCF-ITS, AAKE-BIVT, IIoT-QRSCA, and BASF-ITS, using several metrics, including both computational and communication costs. The effectiveness and security of the proposed model are also verified through a security analysis. The results confirm that the proposed system outperforms similar existing baseline models.
As vehicles become increasingly intelligent, the Internet of Vehicles (IoV) is gaining widespread popularity. However, security concerns in the IoV are becoming increasingly critical, particularly regarding vehicle identity authentication, which directly affects system stability and trust. Therefore, designing an efficient and secure authentication mechanism has become an essential challenge in IoV development. This study focuses on the identity authentication problem between vehicles and roadside units (RSUs) in the IoV. To address the computational overhead challenges posed by the rapid increase in the number of vehicles, we propose a lightweight authentication scheme based on a zero-knowledge proof and design a batch verification mechanism for it. Furthermore, to address the dynamic nature of vehicular traffic, we introduce a Federated Intelligent Dynamic Batching Algorithm (FIDBA) based on a lightweight Multi-Layer Perceptron (MLP). This algorithm dynamically predicts the optimal batch size to minimize verification latency and computational cost. This approach ensures security while significantly reducing computational and communication costs, improving scalability and real-time performance. To further address delays in the handover process, we are integrating blockchain technology. Leveraging its decentralized, immutable, and transparent nature, the blockchain securely stores authenticated vehicle information, avoiding repeated verifications and reducing handover delays. To mitigate inefficiencies in blockchain queries, we design a caching mechanism that enables fast data access. Through theoretical analysis and experimental validation, this study demonstrates the superiority of the proposed scheme in security, efficiency, and scalability. This scheme meets current IoV needs and provides a promising reference for future intelligent transportation systems.
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 deepâlearning 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 blackâhole nodes, a special twinâattentionâbased 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 accuracyâaware energyâefficient multipath routing algorithm (AEMRAP), which takes into account nodeâ and linkâstability 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 proofâofâstake (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 packetâdelivery ratio of 92.3%.
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 Shamirâs 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.
Electric vehicles (EVs) are increasingly central to sustainable mobility, yet their integration into smart city infrastructures remains limited by centralized data systems that restrict scalability, threaten user privacy, and heighten exposure to cyber risks. Most prior work has concentrated on technical performance, with little attention to governance requirements such as accountability, regulatory compliance, and citizen trust. To address this gap, this study develops a decentralized EV data management framework based on IOTAâs Tangle, a distributed ledger designed for secure, scalable, and fee-less interactions across vehicles, charging stations, and urban digital platforms. Using a comparative design, conventional blockchain systems are evaluated against IOTA through simulation results. Findings show that the IOTA-based model sustains faster validation, eliminates fees, and supports higher throughput, while also aligning with governance principles reflected in policies such as the GDPR. The study demonstrates how distributed ledgers can advance both technical feasibility and trust in EV ecosystems.
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 aspectsâinformation 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.
Blockchain has moved from a cryptocurrency infrastructure to a coordination technology for modern communication systems. This review examines how blockchain is being embedded into next-generation communication environments, with particular attention to Internet of Things deployments, edge-cloud collaboration, cyber-physical infrastructures, security and privacy management, smart grids, vehicular networking, and emerging 5G/6G ecosystems. Following the logic of recent survey work on blockchain-enabled communications, the article synthesizes representative peer-reviewed studies, clarifies the blockchain mechanisms that matter for communication engineering, and organizes the literature around application layers rather than isolated protocols. The review shows that blockchain creates value when communication systems require shared trust, auditable automation, decentralized identity, incentive-compatible coordination, or tamper-resistant data exchange across organizational boundaries. At the same time, real deployment remains constrained by throughput, latency, storage overhead, interoperability, privacy leakage, governance complexity, and uneven energy efficiency across consensus designs. Building on both communication-network research and information-systems scholarship, the article develops an integrated analytical view of when blockchain genuinely improves communication architectures and when lighter coordination mechanisms are preferable. The paper concludes by identifying future directions around lightweight consensus, AI-native blockchain orchestration, cross-chain communication fabrics, privacy-preserving verification, and programmable trust for 6G and autonomous infrastructures.
Modern vehicles accumulate fragmented lifecycle records across OEMs, owners, and service centers that are difficult to verify and prone to fraud. We propose VehiclePassport, a GAIA-X-aligned digital passport anchored on blockchain with zero-knowledge proofs (ZKPs) for privacy-preserving verification. VehiclePassport immutably commits to manufacturing, telemetry, and service events while enabling selective disclosure via short-lived JWTs and Groth16 proofs. Our open-source reference stack anchors hashes on Polygon zkEVM at <$0.02 per event, validates proofs in <10 ms, and scales to millions of vehicles. This architecture eliminates paper-based KYC, ensures GDPR-compliant traceability, and establishes a trustless foundation for insurance, resale, and regulatory applications in global mobility data markets.
As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes-including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers-based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5-24.3%, achieves a task success rate of 84.2-88.5%, improves average resource utilization to 88.9-92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs.
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.
Sergej GriÄar, Christian StipanoviÄ, Tea Baldigara
As climate change concerns, urban congestion, and environmental degradation intensify, cities prioritise cycling as a sustainable transport option to reduce CO2 emissions and improve quality of life. However, rampant bicycle theft and poor security infrastructure often deter daily commuters and tourists from cycling. This study explores how advanced security measures can bolster sustainable urban mobility and tourism by addressing these challenges. A mixed-methods approach is utilised, incorporating primary survey data from Slovenia and secondary data on bicycle sales, imports and thefts from 2015 to 2024. Findings indicate that access to secure parking substantially enhances usersâ sense of safety when commuting by bike. Regression analysis shows that for every 1000 additional bicycles sold, approximately 280 more thefts occurâequivalent to a 0.28 rise in reported theftsâhighlighting a systemic vulnerability associated with sustainability-oriented behaviour. To bridge this gap, the study advocates for an innovative security framework that combines blockchain technology and Non-Fungible Tokens (NFTs) with encrypted Quick Response (QR) codes. Each bicycle would receive a tamper-proof QR code connected to a blockchain-verified NFT documenting ownership and usage data. This system facilitates real-time authentication, enhances traceability, deters theft, and builds trust in cycling as a dependable transport alternative. The proposed solution merges sustainable transport, digital identity, and urban security, presenting a scalable model for individual users and shared mobility systems.