Ru Li, Jie Cui, Lu Wei, Irina Bolodurina · 6 authors
In vehicular ad hoc networks (VANETs), distributed identity authentication provides the foundation for securing sessions among entities over wireless channels while eliminating single points of failure. However, existing distributed authentica tion schemes for VANETs typically make unrealistic assumptions about node reliability and trustworthiness, failing to account for scenarios where authentication nodes may be compromised or collude with vehicles. Moreover, these schemes expose the com munication process to linkability attacks while allowing vehicles to self-register their public keys. To address these limitations, we propose a privacy-preserving and accountable distributed collab orative authentication scheme for VANETs that is resilient to ma licious nodes. Using threshold signature techniques, distributed authentication nodes collaboratively perform decentralized ve hicle identity authentication using a predefined threshold. Zero knowledge proof protects the privacy of the signing process while maintaining accountability and effectively preventing malicious behavior by nodes under external or internal adversarial attacks. Furthermore, vehicles self-register their public keys via smart contracts and blockchain technology, ensuring anonymity and unlinkability during registration while enabling the traceability of malicious vehicles. Security and performance analyses show that the proposed scheme enhances the security and robustness of distributed collaborative authentication in VANETs, achieving a better balance between computational and communication costs than existing schemes
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, security vulnerabilities, and trust in large-scale urban deployments. To address these challenges, this paper proposes a blockchain-based privacy-preserving framework for smart mobility services that integrates geo-indistinguishability, pseudonymous authentication, Zero-Knowledge Proofs (ZKPs), and Proof-of-Authority (PoA) consensus into a unified architecture. The framework ensures end-to-end privacy by combining calibrated location obfuscation with decentralized transaction validation and immutable auditability, thereby mitigating both inference-based attacks and reliance on centralized trust. The proposed framework was evaluated using the TAPAS Cologne mobility dataset, comprising 1,000 simulated vehicles and 20 block-chain validators. Experimental results demonstrate that adversarial inference accuracy is reduced to below 12%, while approximately 75% navigation utility is preserved at balanced privacy budgets. Security analysis confirms robust protection against tracking, replay, Sybil, and collusion attacks, with replay attack success rates reduced from 70% to 2% through the enforcement of timestamps and nonces, along with cryptographic verification. Performance evaluation demonstrates that the framework achieves high throughput (1,200 transactions per second) with sub-second latency (0.8 seconds) under realistic transaction loads. Storage growth is optimized to 2.1 GB per million transactions, and the PoA consensus mechanism achieves approximately 30% lower energy consumption compared to Proof-of-Stake-based designs. In addition, resilience ex-periments confirm Byzantine fault tolerance under up to 30% malicious validator participation, without service degradation. Overall, the results demonstrate the practical feasibility of deploying the proposed framework in real-world smart mobility ecosystems that require simultaneous privacy preservation, scalability, and energy efficiency. The framework represents a significant step toward trustwor-thy, privacy-aware, and sustainable smart-city mobility infrastructure, providing a robust foundation for next-generation decentralized mo-bility services.
A growing requirement for intelligent transportation systems (ITS) and vehicle-to-infrastructure (V2I) communication provides the foundation for a safe, highly-scalable, and decentralized tolling and real-time data exchange solution. Blockchain solutions typically incorporate the overhead burden of using consensus algorithms such as PBFT, which do not provide the scalability required in dynamic vehicular environments. This thesis proposes a decentralized V2I communication and data exchange mechanism, based on using a Proof of Stake (PoS) Blockchain. Smart contracts facilitate toll collection, transaction confirmation, and record keeping for V2I vehicle communication via an unbiased blockchain ledger that is immutable. Therefore, the proposed framework creates transparency, tamper-resistance, and data security for the exchange of V2I communication. This work achieves an improvement in communication infrastructurerelated latency and scalability, allowing for applications in many different real-time V2I cases by substituting the communicationintensive nature of POS for the consensus-based approach of existing blockchain systems.
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 blockchain’s 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 introduction of Vehicle-to-Everything (V2X) communications is a fundamental requirement for the evolution of today’s Autonomous Driving, but it leads to a new set of vulnerabilities in network infrastructure. It is important to note that cyber-attacks, including the availability ones, such as DoS, represent a significant threat to the safety of Intelligent Transport Systems (ITS). Traditional signature-based Intrusion Detection Systems (IDS) have a disadvantage in security due to their inability to adapt and manage these new and evolving attacks: they can be blind to new or “zero-day” kinds of attacks. This project is to solve this problem by proposing and validating an unsupervised Intrusion Detection System using a Deep Autoencoder architecture. Unlike typical supervised models, where labelled attack data is needed, this system is trained on normal network traffic patterns only. It tracks anomalies by learning to compress and reconstruct legitimate traffic features, marking large reconstruction errors as malicious intrusions. The model was developed in TensorFlow and tested against the KDD Cup 99 benchmark dataset. Experimental results show the high performance of the system with a total Accuracy of 99.49% and a critical Recall of 99.86%, effectively suppressing almost all availability attacks. In addition, the model is consistent with a Matthews Correlation Coefficient (MCC) of 0.9530, confirming its robustness and reliability even for very asymmetric network traffic. This research establishes solid proof-of-concept for the concept that unsupervised deep learning can work as a powerful new mechanism of security architecture for V2X infrastructure without relying on prior knowledge about specific attack signatures.
Vehicular Ad Hoc Networks (VANETs), as the core infrastructure of intelligent transportation systems, face critical security challenges such as privacy leakage and difficulties in tracing malicious behavior due to open wireless channels and high node mobility. While blockchain-based identity authentication offers inherent advantages like decentralization, making it suitable for the distributed networking scenarios of VANETs, current solutions still suffer from notable shortcomings, including a lack of compliance with the SM9 standard and insufficient regulatory oversight. To resolve the fundamental tension between vehicle identity privacy protection and regulatory traceability in VANETs, this paper proposes an efficient SM9-ABS vehicle identity authentication scheme on blockchain for VANETs. By optimizing SM9 parameters, the scheme integrates Attribute-Based Signature (ABS) and Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) primitives to construct an efficient fine-grained attribute signature generation and verification algorithm, ensuring both security and computational efficiency. An SM9-based threshold identity tracing mechanism is designed to prevent single-point authority abuse, achieving privacy-preserving authentication with controllable accountability. Finally, a complete formal security proof is provided under the q-Strong Diffie-Hellman (q-SDH) hardness assumption, and comparative experiments demonstrate the superior comprehensive performance of the proposed scheme.
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 Internet of Vehicles (IoV) cloud platform enables multi-dimensional sharing of vehicle data, enhancing both its value and the quality of vehicle services. To ensure dynamic authorized access control and safeguard user privacy in the IoV cloud platform, an attribute-based encryption (ABE) scheme is employed. However, existing state-of-the-art schemes still struggle to simultaneously address the following challenges: 1) the limitation of single-application scenarios due to the difficulty of achieving cross-primitive ciphertext transformation; 2) the risk of user privacy leakage caused by the inability to fully hide access policies; 3) the potential for malicious accusations against the delegator due to a lack of verifiability. To tackle these issues, we propose PPVTAC, a Privacy-Preserving, Verifiable, and Transformable Access Control scheme for cloud-assisted IoV. Specifically, we leverage a hybrid proxy re-encryption technique to transform ABE ciphertext into identity-based encryption (IBE) ciphertext, making it more suitable for collaborative scenarios. A cuckoo filter is introduced to achieve fully hidden policies, thereby protecting user privacy. Additionally, we incorporate non-interactive zero-knowledge proofs (NIZKPs) to ensure verifiability, guaranteeing the correctness of transformed ciphertexts while preventing malicious accusations against the delegator. A further advantage of our scheme is its support for unbounded attribute spaces, eliminating the need for system reboot when adding new attributes. In addition, our scheme requires only a constant number of pairing operations during the decryption and re-encryption phases, regardless of the number of attributes. We formalize a security model and rigorously prove that our scheme achieves adaptive security. Our scheme achieves simultaneously for the first time cross-primitive ciphertext transformation, fully hidden policy, and verifiability while ensuring adaptive security. We implement our scheme in real-world environments and compare it with existing state-of-the-art schemes. Our approach offers a more comprehensive feature set without significant performance trade-offs, making it highly suitable for cloud-assisted IoV scenarios.
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.
Vehicle platoon, an increasingly significant technology in vehicular ad hoc networks (VANETs), effectively reduces energy consumption and environmental pollution, mitigates traffic congestion, and enhances road capacity and traffic safety. Collaborative communication between platoons is available to promote the reliability of data dissemination, ameliorate driving strategies, and further strengthen traffic efficiency. Nevertheless, existing efforts for secure multi-platoon data dissemination still face the following issues: (i) lack of a direct authorization strategy for platoons working on different systems; (ii) no effective mechanism to protect vehicle privacy during platoon communications; (iii) absence of a practical verification approach for cross-system ciphertext transformation. This paper builds a privacy-preserving and verifiable cross-system authorization (PVCA) scheme to mitigate the above issues. Specifically, we first put forth the optimized anonymous identity-based broadcast encryption (IBBE) and policy-hiding attribute-based encryption (ABE) protocols to adapt platoon communications while protecting identity and attribute privacy. After that, the authorized token bridging the proposed protocols is designed to enable ciphertexts of IBBE format to be transformed into new ciphertexts of ABE format, enabling flexible authorization. Furthermore, inspired by the Fujisaki-Okamoto transformation, we devise an efficient zero knowledge proof of knowledge protocol, making our PVCA achieve verifiability and fairness. Rigorous security proof and analysis demonstrate that our PVCA is not only secure against chosen plaintext attacks and collusion attacks, but also satisfies the necessary security requirements. We implement a prototype of PVCA (on a desktop computer and Raspberry Pi) and provide a simulation utilizing NS-2 to validate its feasibility for platoon communications in VANETs. The results indicate that, compared to the state-of-the-art ciphertext transformation solutions, PVCA reduces the running time for original ciphertext generation, transformation, and decryption by at least 91.57%, 49.87%, and 50%, respectively, while compressing the original ciphertext and authorization token sizes by over 78.07% and 15.20%.
Ashutosh Kumar, Manik Malhotra, A. Anjali, Amrendra Singh Yadav
Vehicular Ad Hoc Networks (VANETs) require ultra-low-latency authentication in high-mobility scenarios, decentralized trust, and emerging post-quantum security threats. This paper proposes a lightweight blockchain-assisted group authentication framework that integrates post-quantum cryptography with decentralized trust management. The scheme leverages CRYSTALS-Kyber for secure group key distribution, CRYSTALS-Dilithium for message authentication, and zero-knowledge proofs to achieve conditional anonymity and unlinkability. A permissioned Hyperledger Besu blockchain logs encrypted group keys and dynamically updated roadside unit reputation scores, enabling scalable and accountable authentication without monetary transaction overhead. Formal security analysis under the Dolev–Yao adversary model demonstrates confidentiality, authentication, replay resistance, and unlinkability based on standard post-quantum assumptions. Performance evaluation using OMNeT++ and SUMO shows up to 52.7% reduction in communication overhead and a group key update latency of 1.2 ms in dense traffic scenarios, while maintaining high throughput and effective Sybil attack detection. These results highlight the practicality of the proposed framework for post-quantum secure VANET authentication.
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.
Aditya Pathak, Alark Kansara, Irfan Al-anbagi, M. Shamim Hossain
In emergency response vehicular networks (ERVNs), where each second might dictate a life-or-death outcome, the performance of underlying communication systems is critical. Blockchain technology is a promising backbone for authenticating emergency response vehicles (ERVs) at smart traffic signal intersections. However, the decentralized nature of blockchain networks makes them vulnerable to distributed denial of service (DDoS) attacks. A DDoS attack can compromise safetycritical functions by overwhelming the network with fake authentication requests. Therefore, this article presents a comparative performance analysis of two well-known blockchain technologies, namely Ethereum (permissionless) and Hyperledger Fabric (HLF, permissioned), under DDoS-based network congestion. We clarify that this article does not propose any DDoS detection or mitigation mechanisms; rather, we evaluate the ability of the abovedescribed blockchains to maintain service for legitimate transactions under DDoS attacks. We design three DDoS attack scenarios that simulate realistic threats to ERVNs and benchmark throughput and latency using Hyperledger Caliper. Our results show that HLF, because of its architecture, achieves up to 2.7 times higher throughput and lower latency compared to Ethereum. This finding provides an important insight: A permissioned blockchain offers better performance under DDoS attacks, making it a more reliable communication system for safety-critical transportation systems.
Muhammad Umar Farooq Qaisar, Weijie Yuan, Lin Zhang, Shehzad Ashraf Chaudhry · 6 authors
Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.
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 public–private 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 Diffie–Hellman 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.
Decentralized energy trading among electric vehicles (EVs) and charging stations (CSs) still suffers from high energy consumption, low throughput, and heavy consensus overheads. We present Reputation-Aware Proof-of-Energy (RPoE), a lightweight consensus that combines an energy score (ES) and a reputation score (RS); nodes with RPoE above a threshold participate in validation, and the highest-scoring node proposes the block. To sustain participation, we introduce a budget-balanced incentive with a guaranteed participation floor and a proportional share derived from concave weights of normalized RPoE and exchanged energy; tunable parameters trade fairness for efficiency. Our security analysis provides formal guarantees of double-spend resistance, Sybil resistance (trust-authority-backed identities), liveness under bounded delays, and resilience to DDoS/eclipse through eligibility gating. On a multi-node Raspberry Pi (RPi) testbed, RPoE reduces CPU workload by 47%, bandwidth by 5%, and energy consumption by 10% relative to Practical Byzantine Fault Tolerance (PBFT), Proof-of-Stake (PoS), and Proof-of-Authority (PoA), while achieving higher throughput and lower confirmation latency. The incentive remains fair and balanced. In a 10-node study with a participation floor of 20%, Jain's Fairness Index (JFI) is 0.853 (values closer to 1 indicate more fair splits) and the Gini coefficient is 0.221 (values closer to 0 indicate more equal splits), demonstrating equitable and budget-balanced rewards that avoid dominance.