ABSTRACT Smart cities are digitally advanced urban environments that are equipped with sensor networks to gather, share, and analyze extensive data across interconnected systems. Among various smart city applications, the intelligent transportation system represents one of the most critical and securityâsensitive domains. An intelligent transportation system relies heavily on continuous vehicular communication, a lowâlatency decisionâmaking process, as well as realâtime traffic monitoring. Existing Internet of Things security methods encounter significant computational overhead and limited scalability, making them unfit for realâtime applications. To address these issues, this paper proposes a novel security model, named Deep Residual Stacked Bidirectional Network. The proposed system is integrated into a blockchainâsupported hybrid system to ensure security and privacy for users and systems in smart cities. This enhanced DeepâLearning model combines the residual learning power with bidirectional long shortâterm memory layers. To effectively manage deeper networks, residual connections help mitigate the vanishing gradient problem, while bidirectional long shortâterm memory provides sequential dependencies in backward and forward directions. This allows the model to detect patterns in data, especially in security environments where data is highly dynamic and timeâsensitive. Four Internet of Thingsârelated datasets are used to evaluate the efficiency of the developed algorithm. These datasets offer various realâworld network traffic and attack scenarios that allow comprehensive performance evaluation of the proposed approach in comparison with existing methods. The test outcomes revealed that the blockchainâsupported proposed method outperforms traditional methods with an accuracy of 98.21%, specificity of 97.39%, and F1âscore of 97.46%.
Wisam Makki Alwash, Weam Husham Aljabbari, Muhammed Ali Aydın, Hasan H. Balık
Internet of Vehicles (IoV) intrusion detection systems (IDSs) require collaborative learning that preserves raw-data locality while producing independently checkable post-run evidence. This paper presents FL-BC-IDS, an evidence-native, privacy-aware hierarchical federated IDS in which vehicles train Differentially Private XGBoost models, roadside units perform deterministic admission and tree-bagging aggregation, and the GLOBAL stage forms an equal-weight ensemble over validated RSU models. Signed reports, privacy records, SHA-256/Poseidon commitments, scoped Groth16 proofs, reconstructable public inputs, and digest-pinned blockchain receipts provide a unified verification path. Across 10 seed-controlled runs, the mean ± SD accuracy/F1 values were 0.998021±0.000246/0.983597±0.002053 on CSE-CIC-IDS2018 and 0.999867±0.000152/0.999495±0.000579 on CICIoV2024. With thresholds fixed exclusively from development data, the strict held-out-attack macro recall was 0.8031 and 0.9090 on CSE-CIC-IDS2018 and CICIoV2024, respectively, indicating residual attack-specific generalization limitations; supervised rolling-origin temporal refresh on CSE-CIC-IDS2018 achieved 0.984788 pooled seen-attack recall at a 0.005700 test FPR. A controlled 20-vehicle, eight-round heterogeneity and participation stress test retained 0.998151 accuracy and 0.984782 F1-score. Verification rejected invalid or context-mismatched artifacts and independently checked modelâanchor consistency, RSU aggregation replay, commitments, and public inputs. The reported DP budgets are conditional learner-stage bounds for learner-input record instances, not end-to-end guarantees for original pre-preprocessing records.
Internet of Vehicles (IoV) security mechanisms often classify isolated messages or assign node-level trust scores, yet these decisions do not explain whether a malicious but authenticated event has distorted the intended evolution of traffic. This paper proposes IntentProv-IoV, a causally grounded provenance framework for traffic-intent preservation in V2X environments. Traffic intent is modeled as the short-horizon collective state expected under non-adversarial conditions, and deviation is measured between predicted and observed traffic states. The framework constructs temporal provenance graphs linking vehicles, roadside units (RSUs), cooperative perception outputs, prediction nodes, and traffic-control decisions. To remove the ambiguity of marginal contribution, node contribution is formalized as an interventional effect in a structural causal model and estimated through Monte Carlo counterfactual edge-weight attenuation, with a linear sensitivity fallback for real-time edge deployment. A calibrated composite score integrates anomaly evidence, traffic-intent deviation, trust risk, and provenance contribution. The evaluation design compares IntentProv-IoV with detection, trust, blockchain trust, graph anomaly, Granger causal, structural causal, and counterfactual GNN baselines and includes predictor sensitivity, adaptive adversaries, prediction noise, packet loss, trajectory-only real-data validation, and edge overhead. Simulation-scale results indicate improved attribution precision, stronger traffic-intent deviation reduction, and edge-suitable latency. By shifting V2X security from message-level detection to causally explainable traffic-intent assurance, IntentProv-IoV provides a more accountable security objective for cooperative vehicular systems.
Roadside edge infrastructure is becoming an active execution layer in connected transportation. Roadside units, edge servers, and vehicular gateways not only relay safety messages, but also run local services and support traffic-control decisions. Once these nodes are compromised, data authentication, blockchain logging, or forensic evidence management cannot by itself guarantee service trustworthiness, because the node that senses, signs, forwards, or processes the information may already be running an unauthorized software stack. This paper presents RISE-CT, a runtime-integrity assurance and self-healing recovery framework for roadside edge infrastructure. RISE-CT models each node through a layered security-state graph covering hardware roots, boot chains, firmware images, runtime processes, key usage, and service status. It combines boot-time, periodic, and event-triggered attestation with runtime drift diagnosis to detect firmware deviation, process injection, abnormal key access, replayed evidence, and service-behavior changes. When the risk score exceeds policy thresholds, a risk-aware admission state machine moves the affected node into degraded, quarantined, or recovering states, and coordinates service migration, key renewal, firmware rollback, and re-attestation before safe re-admission. Seed-controlled emulation under firmware tampering, malicious OTA updates, runtime injection, key misuse, replayed attestation, and roadside-service hijacking shows that, under the evaluated emulation settings, RISE-CT reduces the representative detection delay to 0.98 s and service interruption to 0.82 s, while achieving a 0.94 re-admission success rate with controlled attestation overhead. The results suggest that runtime node integrity can provide a potential security mechanism for improving the trustworthiness of roadside infrastructure under the evaluated conditions.
Ravindra Janardan Lawande, Sudhir Bapurao Lande, Manisha Lande
Internet of Vehicle (IoV) uses heterogeneous access technologies to link automobiles and their surroundings. Effective methods are essential for safeguarding data confidentiality and privacy during communication among the roadside unit (RSU), the control room, and vehicles. Many vehicle-to-infrastructure authentication-based approaches have been developed to secure the IoV environment. However, efficiency and security are challenged by instability, decentralization, and transaction-tracking features. To resolve this, a secure, lightweight, and scalable communication protocol was developed for a 5G-enabled SDN-IoV environment. Efficient block verification is achieved through the Joint-Graph Delegated Practical Byzantine Fault Tolerance (JtGr-DPBFT) mechanism, in which validators create subgraphs to reduce communication overhead. JtGr-DPBFT is combined with an Improved Gossip Algorithm (IGA) to minimize message redundancy and optimize bandwidth utilization. Moreover, a lightweight hierarchical authentication mechanism, assisted by a Merkle Tree with Boneh-Lynn-Shacham (HAMT-BLS) signatures, enables compact block verification and minimizes computational and communication costs. The proposed model achieves tamper-proof, efficient, and scalable block verification by incorporating hierarchical authentication with consensus optimization. This approach is simulated in the NS3 tool, and performance is evaluated in terms of propagation delay, transaction confirmation latency, throughput, communication cost, and network delay. Thus, secure and tamper-proof communication is developed to ensure integrity, trust, and dependability in the SDN-enabled IoV environment.
Incorporating connected mobility data into decision-making brings about significant data security and privacy challenges in next generation transportation systems. The problem becomes worse when the raw traffic data stream contains commuters' sensitive information that could be extracted by successful attackers. The challenges of data security and privacy must be addressed in order to create a secure information sharing environment for next generation transportation systems. â Blockchain technology, which provides a tamper-resistant journal of state transition events, becomes an ideal candidate for realizing the goals of creating secure information sharing frameworks. However, existing blockchain technology and deployments also have their limitations, especially in data privacy. Because the saved data on the blockchain ledger can not be altered, we do not want to make sensitive information publicly available or record false information permanently without an authentication protocol. â Innovations are needed to overcome barriers in blockchain technology for enabling secure and privacy-preserving information sharing in next generation transportation systems. This Ph.D. dissertation introduces three main innovations: (1) a zero-knowledge and Byzantine fault tolerant consensus that brings privacy-preserving to the blockchain consensus level for verifying and processing transactions, Chapter 2; (2) novel privacy-preserving authentication schemes for blockchain networks based on zero-knowledge proofs to increase security and safety in traffic management, autonomous truck fleets and ridesharing, Chapters 3, 4 and 5; and (3) blockchain-inspired architecture designs with access control policies that protect huge amounts of traffic and users' data and log access events into blockchain for traceability and accountability, Chapters 4 and 5.
The automotive industry is transitioning to Zonal-oriented Architectures (ZoA) for Software-Defined Vehicles (SDVs), enabling frequent over-the-air (OTA) updates for 100+ Electronic Control Units (ECUs). While OTA updates improve efficiency, they introduce safety-critical security risks. Current standards like Uptane and AUTOSAR Adaptive rely on Public-Key Infrastructure (PKI). However, PKI-based authentication creates bandwidth bottlenecks in in-vehicle and vehicle-to-cloud (V2I) communication as ECU density increases. It also risks exposing sensitive vehicle configurations and passenger privacy due to centralized architectures. Next-generation Zonal SDVs require decentralized, scalable authentication with data privacy. To address this, we propose zk-ScalHard, a hardware-rooted, privacy-preserving authentication protocol. We introduce a decentralized, hierarchical trust-promotion model utilizing Silicon Physical Unclonable Functions (PUFs) and two novel Zero-Knowledge Proof (ZKP) circuits: (1) Zonal Identity and Integrity (ZIDI) and (2) High-Performance Computing Aggregation (HPCA). These circuits employ multi-party computation (MPC) and recursive aggregation to achieve decentralization and scalability. The integration of ZKPs and PUFs ensures 100% vehicle-level data sovereignty. Benchmarked against Uptane, zk-ScalHard achieves constant O(1) communication and verification complexity, improving upon the linear O(n) complexity of current systems. Evaluation shows a 99.2% reduction in authentication bandwidth and a 99.9% reduction in the temporal attack surface. Our results demonstrate that zk-ScalHard provides a scalable, secure, and GDPR-compliant architecture for future Zonal SDVs.
Open access
3 source records
cs.CR
Physical Unclonable Functions (PUFs) and Hardware Security
Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.
Proof-of-Stake DAG-BFT consensus faces a trilemma between sybil resistance, reward fairness, and plutocracy. Existing protocols prioritize liveness over fair stake-based selection, driving longitudinal centralization. FairWave is a dual-channel DAG-BFT protocol that separates anchor selection from reward distribution. The selection channel is super-linear in stake, guaranteeing Sybil gain < 1 for K > 1; the reward channel is sub-linear via square-root stake normalization. DAG-derived uptime and latency metrics eliminate external oracles,and lagged reputation breaks circular dependency between selection outcomes and weights. Evaluated through approximately 550,000 Monte Carlo rounds against eight baselines, FairWave shows Gini 0.140 (vs. Pure-PoS 0.490, monotone HHI reduction from 0.039 to 0.020 over 50,000 epochs, and optimal Sybil split K * = 1. Safety follows unconditionally from the 2f + 1 commit rule; the liveness model predicts monotone degradation from 94.0% at b = 0.20 to 74.0% at b = 1/3, consistent with the architectural expectation of no discontinuous cliff.
Mohammad Alsaffar, Eman Abouelkheir, Wedad Alawad, Majed S. Alsayfi · 8 authors
The ultra-dense vehicle scenarios envisioned in 6G put high requirements on ultra-low latency, secure cooperation, and efficient task offloading decisions. Existing systems usually optimize latency or energy independently but ignore joint privacy problems and long-term trust sustainability. In this work, a distributed intelligence architecture based on the combination of federated learning (FL) and blockchain based trust management for vehicle-to-vehicle (V2V) edge computing is proposed. The proposed architecture enables collaborative prediction and decentralized incentive enforcement in a privacy-preserving manner without revealing raw vehicle data. In this paper, task allocation is defined as a multi-objective optimization problem, which jointly considers latency, energy consumption, communication stability and privacy exposure. The resultant problem is addressed by a learning-coupled primal-dual optimization, where the federated prediction is used to drive the offloading decisions and the dual update is used to impose the limitations of the system. A light-weight distributed ledger layer ensures secure coordination, automatic incentive allocation and reliable detection of fraudulent nodes. The extensive simulations in the integrated traffic-network-blockchain environments show that the proposed method outperforms the state-of-the-art baselines, achieving up to 30-40% reduction in the service latency, approximately 25% improvement in task completion rate, enhanced privacy preservation by the gradient-based learning, and up to 95% accuracy in detecting the malicious nodes. These results validate the efficacy of the suggested framework for attaining scalable, privacy-aware, and trustworthy distributed intelligence for next-generation 6G vehicular edge networks.
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.
In the Internet of Vehicles (IoV), the large-scale deployment of smart vehicles has triggered new road traffic safety challenges. Particularly, existing vehicle accident report transmission schemes still face challenges such as privacy leakage, Single Point of Failure(SPOF), physical cloning attacks, and excessive computational overhead. To address these issues, this paper proposes a secure accident report transmission scheme that uses Non-Interactive Zero-Knowledge Proof (NIZKP) and Physically Unclonable Functions (PUF). This paper designs a decentralized authentication scheme for vehicle registration that prevents SPOF and privacy leakage. We also use the PUF to realize two-factor authentication login, which effectively resists physical cloning attacks. In addition, the authentication process uses NIZKP based on the Pedersen commitment to realize authentication for accident report coordination. At the end of the accident report coordination, it is passed into the blockchain for storage, realizing the secure transmission of accident reports. To reduce the storage as well as computation overhead, this paper uses a key derivation function to update the key. Finally, formal security analysis was conducted using the Real or Random (ROR) model and the ProVerif tool, the results prove that the proposed protocol meets security requirements. Comparing our proposed scheme with related schemes, the computational overhead of our V2V scheme is reduced by 42.4%, with higher security and lower communication overhead.
Open access
Vehicular Ad Hoc Networks (VANETs)
Physical Unclonable Functions (PUFs) and Hardware Security
M. Lavanya, V Thiruppathy Kesavan, G. Sathya, R. Gopi
Electric Vehicles (EVs) that use Internet of Things (IoT) networks often involve the exchange of sensitive data between vehicles, charging stations, and other infrastructure, making data security and user privacy critical concerns. Existing methods for securing data in EV IoT networks rely on centralized systems, which create a single point of failure and are vulnerable to cyberattacks, data breaches, and unauthorized access. Furthermore, these systems struggle to address privacy concerns effectively, especially regarding user location and personal information. The proposed solution introduces a Blockchain Technology-based privacy preservation framework for EV networks (BCT-PP-EV). This framework leverages blockchain's decentralized nature to provide secure, transparent, and tamper-proof data exchanges. It ensures user privacy using cryptographic techniques such as zero-knowledge proofs (ZKP) and data anonymization, allowing privacy-preserving transactions without compromising data accuracy. Blockchain's immutability guarantees the integrity of the shared data, while smart contracts automate secure and efficient interactions within the network. The proposed method enhances secure data sharing while preserving privacy across EV IoT networks. By decentralizing data storage and enabling transparent auditing, BCT-PP-EV fosters trust among stakeholders and reduces the risks of unauthorized access or data manipulation. Preliminary findings suggest that implementing BCT-PP-EV significantly improves the security and privacy of data exchanges in EV networks, providing a scalable and resilient solution for the evolving smart transportation ecosystem. Experimental results demonstrate that BCT-PP-EV achieves 94.91% secure data sharing efficiency, reduces data breaches by 92.84%, and ensures data accuracy of 91.44%. Additionally, the framework exhibits high scalability of 96.57% with increasing network nodes, while maintaining controlled latency and throughput. Although unauthorized access resistance is measured at 24.71%, indicating scope for further improvement, the overall results confirm that BCT-PP-EV provides a robust, scalable, and privacy-preserving solution for next-generation smart transportation systems.
The increasing reliance on centralized ride-sharing structures, and exposes users to risks such as system failures and privacy breaches. manipulation, single points of failure, and privacy violations. In addition, high commission fees imposed by such platforms reduce the net earnings of drivers and compromise fairness within the ecosystem. To address these inefficiencies, this project introduces a decentralized vehicle borrowing system and carpooling, based on Ethereum Compatible blockchain and smart contracts. The proposed platform eliminates intermediaries by allowing KYC-verified drivers, passengers, and vehicle owners to interact directly, thereby building trust and operational transparency. All ride postings, bookings, car borrowing transactions, and agreement verifications are recorded immutably through smart contracts. Identity proofs, vehicle documents are cryptographically signed through MetaMask and uploaded via a decentralized file system (IPFS), ensuring authenticity and wallet-to-user binding. For drivers who borrow cars, temporary verification is enabled after signing a smart-contract-based agreement linked to the vehicle's verified owner. To maintain decentralization without depending on an administrator, the system introduces a Global Dispute Center where only users who fulfill certain predefined conditionsâhaving verified their identityâcan participate in resolving concerns through a voting process. This decentralized decision-making process enhances fairness and trust. Additionally, a structured post-ride rating system builds mutual accountability and trust among participants, while integrated COâ tracking encourages environmentally conscious behavior. Together, these features help minimize traffic load, support conscious travel habits, and build a reliable, user-governed mobility system that is secure, transparent, and environmentally supportiveâfunctioning entirely without any centralized authority or administrative oversight, thereby ensuring long-term sustainability. To address these limitations, this project proposes a blockchain-powered peer-to-peer carpooling and vehicle borrowing system that enables direct interaction between passengers, drivers, and vehicle owners without intermediaries. The platform utilizes smart contracts to automate agreements, MetaMask for secure authentication, and IPFS for decentralized storage of essential records. By shifting operational control to users, the system enhances transparency, fairness, and reliability in transactions. Conventional mobility services also face issues such as opaque processes, inefficient dispute handling, and limited mechanisms for conflict resolution. Drivers often lose a substantial portion of their income to service fees, while users lack trust in centralized decision-making systems. Furthermore, minimal emphasis is placed on promoting environmentally responsible travel practices. Motivated by the need for an open and community-driven mobility platform, this research aims to establish a distributed ecosystem that eliminates third-party dominance and ensures tamper-proof record keeping. The system incorporates KYC-based digital identity verification, smart contract-enforced agreements, decentralized dispute resolution through voting, and COâ emission tracking to encourage sustainable transportation. The scope of the project includes enabling secure ride booking, vehicle borrowing under verified ownership, and democratic dispute resolution among verified users. By leveraging distributed networks and digital wallets, the platform presents a scalable and sustainable alternative to centralized ride-sharing models.
Collins Izuchukwu Okafor, Love Allen Chijioke Ahakonye, DongâSeong Kim, Jae Min Lee
Vehicular-to-Everything (V2X) communications promise unprecedented safety and efficiency gains but remain vulnerable to confidentiality breaches such as eavesdropping, traffic analysis, and man-in-the-middle attacks. We propose ConfidSPEC-V2X, a focused hybrid framework that integrates continuous-variable quantum key distribution (CV-QKD), a multi-agent deep reinforcement learning (DRL), and an Ethereum-based permissioned blockchainPureChainpublic-key infrastructure (PKI) to deliver information-theoretic secrecy, dynamic traffic obfuscation, and tamper-proof key management. In the quantum module, CV-QKD transceivers embedded in On-Board Units (OBUs) and Roadside Units (RSUs) establish symmetric keys resilient to passive interception and capable of immediate eavesdropping detection. The Artificial Intelligence (AI) module employs multi-agent DRL agents at RSUs to learn optimal dummy-traffic injection policies that obfuscate real V2X message patterns against statistical inference. The blockchain module leverages PureChain smart contracts to register, rotate, and timestamp vehicle public keys, ensuring that any man-in-the-middle attempt to forge or replay keys is invalidated. We implement and evaluate ConfidSPEC-V2X within an OMNeT++/Veins simulation under realistic urban mobility scenarios, measuring the quantum bit error rate, key generation throughput, obfuscation entropy, and key management latency. Results demonstrate that our framework achieves robust confidentiality protection with minimal performance overhead.
Background: Airport security demands sub-second, high-throughput identity verification while increasingly stringent privacy regulation prohibits the centralized accumulation of passenger data. Existing deployments copy complete passenger profiles to every checkpoint terminal, multiplying the data breach surface at each journey touchpoint and conflicting with GDPR data minimization requirements. Methods: This paper presents BIPV (Blockchain-based Identity and Privacy Verification), a system that resolves this tension through programmable zero-knowledge proofs. BIPV anchors only cryptographic references on a Hyperledger Fabric consortium blockchain; passengers prove eligibility at checkpoints via Circom-compiled Groth16 zk-SNARKs that confirm policy compliance without disclosing any underlying personal attributes. We detail the Circom circuit design for airport policy predicates (AgeVerifier, NationalityChecker, DocumentValidator), a proof pre-computation and caching strategy that eliminates gate-lane latency, and a Hyperledger Fabric consortium governance model that anchors verification keys without recording passenger movement. Results: Our prototype achieves 0.42 s mean verification latency, 2,380 passengers per checkpoint per hour, and a 94.7% reduction in PII exposure relative to centralized baselines, evaluated across 1,000 simulated verification sessions. Security analysis confirms resistance to credential forgery, replay attacks, and consortium collusion under standard cryptographic assumptions. Conclusions: BIPV satisfies GDPR data minimization requirements, ICAO Annex 17, and IATA One ID guidelines. Beyond aviation, the BIPV model generalizes to any domain requiring high-assurance, high-throughput identity verification under privacy obligations.
Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load.
Vehicle trajectory prediction in Internet-of-Vehicles requires collaborative learning over sensitive trajectories under intermittent connectivity and partially trusted participants. ChainDrive-FL-VRA coordinates semi-asynchronous federated learning on a permissioned consortium ledger using Practical Byzantine Fault Tolerance (PBFT), while keeping raw trajectories and raw model-update tensors off-chain. Each client submits an on-chain header containing a commitment and hash of the local update, together with zero-knowledge proofs that certify [Formula: see text]clipping and anchor-consistency. Validators admit only proof-checked updates, compute staleness- and reputation-aware robust weights, and publish a proof of correct aggregation that binds the aggregation commitment and the committed global model hash to the admitted committed updates under fixed-point weights. A contextual-bandit trigger selects aggregation timing under client churn. Experiments on NGSIM US-101 and I-80 show improved ADE/FDE/RMSE and improved robustness under staleness and anomalous updates, while on-chain artifacts remain at kilobyte scale per update and per aggregation event.
Shuchi Priya, Sushil Kumar, Anjani Anjani, Ahmad M. Khasawneh · 5 authors
Smart vehicles integrated with the Internet of Things (IoT) provide rich data for traffic management, safety, and liability services; however, existing blockchain-enabled vehicular architectures still struggle with consensus scalability, heavy centralized validation, limited interaction-based corroboration, incomplete attack coverage, and rapid ledger growth. In particular, many schemes either optimize single-layer consensus or embed detailed reputation information into every transaction, while pushing most validation to central servers. This leads to bottlenecks under dense traffic and leaves replay, Sybil-assisted 51% attacks on roadside units (RSUs), and man-in-the-middle tampering only partially addressed. In this context, this paper proposes a novel hierarchical blockchain for vehicular IoT (HBV-IoT) model to address the above challenges. An independent transaction for periodic vehicle status reporting and an interaction-based transaction for corroborating data between vehicles in proximity are presented. Three smart contracts are designed to automate the validation and processing of transactions, and to identify compromised or malicious vehicles within the HBV-IoT network. Algorithms for distributed consensus to accept transactions into the blockchain and for vehicle reputation management to enforce edge-level filtering and down-weighting of malicious nodes are implemented. Simulation results demonstrate significant improvements compared to conventional vehicular blockchain approaches, with performance gains validated by 95% confidence intervals. The model supports practical applications, including real-time traffic monitoring, automated e-challan issuance, intelligent insurance claim processing, and blockchain-based vehicle registration.
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.
The current methods don't meet the security and performance needs of Internet of Vehicles (IoV) apps, and they also don't give the end user a low-latency, secure edge-computing service at the same time, while in the context of vehicles. This study presents a blockchain-enabled edge computing architecture that employs Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) for consensus, aiming to simultaneously enhance latency, energy efficiency, and security. The containerised architecture uses Hyperledger Fabric with Kubernetes to efficiently manage micro-services and move tasks off of them. In urban, suburban, and highway settings, the framework consistently outperforms baseline algorithms, with a 30â45% improvement in end-to-end latency and a 55% reduction in energy use under moderate to heavy loads. The system finished more than 95% of its tasks while keeping block consensus times under 1.2 seconds at peak loads. The architecture also showed consistent performance with different levels of vehicle density and used zero-knowledge proofs with attribute-based security to protect data from cyber threats from bad actors. These findings indicate that the integration of DDQN and blockchain will mitigate security issues in the Internet of Vehicles (IoV) by enabling secure edge computing for future vehicular networks.
In this paper, a novel security framework for industrial internet of things (IIoT) and vehicular networks is proposed, integrating blockchain technology with advanced encryption and data classification mechanisms to enhance data integrity, confidentiality and trustworthiness. The work employed ChaCha20-Poly1305 encryption to safeguard the data transaction to local cluster nodes. A private blockchain gateway then processes the encrypted data, classifying it based on confidentiality levels, and directing storage either to cloud servers or the interplanetary file system (IPFS). To ensure data integrity, a proof of authority consensus mechanism within the blockchain is incorporated, while zero knowledge proof (ZKP) methods are used for authentication and secure data access. Empirical evaluations demonstrate that our framework achieves a data transmission security rate of 97.5%, with an average encryption and decryption latency of 150 milliseconds, significantly improving over traditional methods. The proof of authority consensus mechanism exhibits a transaction validation speed of 300 transactions per second, showcasing enhanced efficiency compared to standard blockchain models. Furthermore, the integration of ZKP challenges results in a 30% reduction in unauthorised access attempts, indicating a substantial improvement in overall security. This work emphasises the need for continuous innovation in addressing the various security issues in IoT, ultimately advancing the operational efficiency and security of these systems.