Federated Learning with Blockchain Approach for Trust Management in IoV

Advanced connection and autonomous features are being made possible by the Internet of Vehicles (IoV), which is causing a revolution in transportation. Strong security measures are required, however, because the prevalence of connected devices also increases the likelihood of cyberattacks and data breaches. This study introduces a new method for protecting IoV networks, which combines Blockchain technology with Quantum Key Distribution (QKD), creating a security architecture with two layers. Internet of Vehicles (IoV) technologies enable autonomous driving and real-time data exchange by connecting vehicles to infrastructure and one another. These advancements make things safer and more efficient, but they also put sensitive information at risk of cyberattacks. Modern security measures are essential since traditional encryption methods are becoming more and more insecure. To provide encryption that is theoretically unbreakable, the suggested system uses QKD to create and distribute cryptographic keys based on principles of quantum mechanics. To improve trust and transparency, blockchain technology is used to record these keys and any subsequent transactions in an immutable, distributed ledger. A hybrid architecture, with QKD securing the key exchange and Blockchain ensuring the integrity and authenticity of the communication, is designed as part of the integration process. Improved security and speed have been shown in simulations and prototype implementations of the QKD-Blockchain architecture in IoV networks. By preventing eavesdropping and key interception, the QKD technique kept the communication channel secure. With an average delay of only about 2 milliseconds, QKD performed admirably and was well below the permitted range for real-time vehicular communications. On average, validation durations for transactions were 5 milliseconds, which was a little overhead due to blockchain integration. The system efficiently handled up to 10,000 transactions per second without affecting security or performance, proving that it can serve massive IoV networks, according to scalability testing. Under high-load scenarios, the framework maintained consistent performance and security, proving its robustness in stress tests. Together, QKD and Blockchain provide a scalable and trustworthy option for future vehicular communication networks, and these results show how feasible and robust it is to use them to protect IoV systems. An intriguing approach to the security issues plaguing IoV systems is the integration of QKD with Blockchain technology. An unparalleled level of protection against cyber threats is provided by the dual-layered system, which guarantees strong encryption and data integrity. This fresh method may lead to improved and more trustworthy IoV networks by establishing new benchmarks for secure vehicular communication. In order to optimize the implementation and tackle any new issues that may arise, more research and development should be conducted.

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL training loss and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that can select the users who can contribute toward improving the FL convergence speed more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time and the FL training loss. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on the global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to improve the global model, the FL convergence speed, and the training loss. Simulation results show that the proposed approach can reduce the FL convergence time by up to 56% and improve the accuracy of identifying handwritten digits by up to 3%, compared to a standard FL algorithm.

The Internet of Vehicles (IoV) marks a revolutionary leap in transportation, integrating vehicles with the Internet to enhance convenience, intelligence, and efficiency. As IoV applications continue to expand, a spectrum of challenges and untapped opportunities emerge. Among these, the security of IoV stands out as a critical issue in transportation systems, given its direct impact on road safety. To this end, this survey conducts an in-depth analysis of IoV security challenges and introduces a distinctive approach by systematically categorizing threats into inside-vehicle and outside-vehicle domains, providing a comprehensive understanding of the full spectrum of risks. Meanwhile, we examine defense mechanisms designed to counter these threats and explore proactive strategies to enhance IoV security. In addition, this study explores emerging protection techniques, such as AI-driven intrusion detection, blockchain-based trust management, and 5G-enabled secure routing, demonstrating how these technologies can be effectively integrated to safeguard IoV systems. Furthermore, this survey provides actionable recommendations and forward-looking research directions to guide the development of real-world implementations, standardized procedures, and regulatory frameworks, benefiting stakeholders, policymakers, and researchers. As IoV continues its rapid evolution, these insights offer a comprehensive roadmap to strengthen IoV security and contribute to a safer, more resilient intelligent transportation ecosystem.

A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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.

To addresses the security and efficiency problem in the Internet of Vehicles (IoV) ecosystem, where vast data exchanges between vehicles and roadside units via smart devices pose potential security, privacy, and data integrity risks. To combat these issues and improve user experience without compromising data quality, a cutting-edge framework that integrates Graph Neural Network with federated learning is proposed. This solution not only secures vehicular data privacy and integrity but also reduces unnecessary data transmissions by enabling local data storage, thus easing the load on communication networks. Additionally, I introduce a consensus mechanism powered by edge computing to minimize latency and enhance data exchange reliability, leveraging distributed computing to mitigate network congestion and scale the IoV infrastructure efficiently. Central to our framework is a reputation system that assesses user contributions, incentivizing honesty and reliability while deterring malicious activities. Our approach promises a more secure, efficient, and scalable IoV environment, showcasing the pivotal role of advanced technologies in addressing current data exchange challenges.

In enhancing transportation, the Internet of Vehicles (IoV) facilitates real-time vehicle-infrastructure interactions, improving traffic management and safety, crucial for autonomous vehicle development. However, high-traffic IoV networks face challenges with traditional Batch Message Verification (BMV) methods, often leading to critical alerts being unverified and dropped due to a lack of prioritization. Addressing this, we introduce an enhanced verification model, “DyBatch: Message Prioritization and Dynamic Batch Verification in Large-scale IoV Networks”, designed for Edge-computing enabled IoV (e-IoV) networks. DyBatch effectively tackles message loss by classifying Basic Alert Messages (BAMs) based on urgency through a flag-based mechanism, coupled with a flexible batch size concept to overcome fixed-size limitations. The model employs Verification-Proxy Vehicles (VPVs), special vehicles that handle verification during Edge Node (EN) bottlenecks. DyBatch aims to minimize message loss and verification delays, enhancing real-time application suitability. Utilizing the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL) and Java 8 on Jetson Nano and Raspberry Pi 4, our analysis reveals a 90% reduction in verification delays, a 91% improvement in computation overhead, and a 67% increase in verified messages, significantly boosting efficiency in high-density IoV environments.

The Internet of Vehicles (IoV) enables technology that allows IoV and vehicles to connect everything. IoV has become an essential component of modern life. This exponential growth of IoV technology has introduced significant security and privacy issues, which pose potential threats to different types of attacks and cause different threats to the normal operation of vehicles. To prevent intelligent vehicle accidents and identify malicious attacks within IoV networks, various researchers have focused on machine learning (ML)-based methods to detect attacks. Intrusion detection systems (IDS) are a prominent solution for cyber attacks in IoV using ensemble learning. To achieve higher accuracy and detection rate, designing an improved detection framework using ensemble learning is a challenging task. The design of an ensemble-based IDS depends on two main challenges: selecting base classifiers and their combination methods. Therefore, in this study, we propose a hybrid ML model to detect various attacks in IoV. We have used different ML algorithms to develop an enhanced algorithm that can efficiently detect attacks in IoV networks. To evaluate the performance of the proposed system, we have used two well-known datasets, (CIC-IDS2017) and (UNSW-NB15). The proposed algorithm shows outstanding performance from the performance results, with an average attack detection accuracy of 99.75% and 100% and an F1 score of 99.74% and 100%, respectively, for both datasets. Further performance scores, that is, recall, precision, and F1 score metrics, validate the exceptional effectiveness of the proposed framework.

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction-coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied to solve the allocation of UAV coalitions. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer to support the IoV components independently and not to form any coalition.

The Internet of Vehicles (IoV) is becoming an interesting topic among researchers and it has emerged as a rapidly advancing field within Vehicular Ad-hoc Networks, facilitating intelligent communication between vehicles and the cloud through the integration of Internet of Things (IoT) technologies. The IoV surroundings face serious challenges due to the highly interrelated nature of vehicles and infrastructure in certifying privacy and security. Traditional approaches to authentication lack the strength required to protect against developing fears, leaving systems vulnerable to attacks. This survey addresses the gap by employing formal analysis approaches to prove authentication protocols, targeting to reinforce safety and confidentiality in IoV systems. The IoV communication model consists of Vehicle-to-Vehicle, Vehicle-to-Infrastructure, Vehicle-to-Personal Devices, and Vehicle-to-Cloud. Smart automobiles are equipped with cameras, radars, on-board units, and sensors to help reduce the number of accidents by giving drivers or autonomous vehicles up-to-date information on roads, traffic signals, and other pertinent entities. As human lives are at risk, security and privacy in the IoV communication paradigm are critical and cannot neglected. Security and privacy breaches may cause accidents because the attacker can inject false information into the system as the communication channel is open and unsecured. The researchers proposed many authentication protocols to provide secure communication between IoV entities. Although surveys on IoV security and privacy issues deal with communication and computation costs, they lack formal analysis of the authentication protocols. This survey reviews the informal analysis and formal analysis methods used by various authentication protocols. Furthermore, the challenges and future work are also included in this survey.

The Internet-of-Vehicles (IoV) can make driving safer and bring more services to smart vehicle (SV) users. Specif-ically, with IoV, the road service provider (RSP) can collaborate with SVs to provide high-accurate on-road information-based services by implementing federated learning (FL). Nonetheless, SVs' activities are very diverse in IoV networks, e.g., some SVs move frequently while other SVs are occasionally disconnected from the network. Consequently, obtaining information from all SVs for the learning process is costly and impractical. Furthermore, the quality-of-information (QoI) obtained by SVs also dramatically varies. That makes the learning process from all SVs simultaneously even worse when some SVs have low QoI. In this paper, we propose a novel selective FL approach for an IoV network to address these issues. Particularly, we first develop an SV selection method to determine a set of active SVs based on their location significance. In this case, we adopt a K-means algorithm to classify significant and insignificant areas where the SVs are located according to the areas' average annual daily flow of vehicles. From the set of SVs in the significant areas, we select the best SVs for the FL execution based on the SVs' QoI at each learning round. Through simulation results using a real-world on-road dataset, we observe that our proposed approach can converge to the FL results even with only 10% of active SVs in the network. Moreover, our results reveal that the RSP can optimize on-road services with faster convergence up to 63% compared with other baseline FL methods.

The Internet of Vehicles (IoV) is an emerging technology that enables vehicles to communicate with their surroundings, provide convenient services, and enhance transportation systems. However, IoV networks can be vulnerable to security attacks because vehicles communicate with other IoV components through an open wireless channel. The recent related work suggested a two-factor-based lightweight authentication scheme for IoV networks. Unfortunately, we prove that the related work cannot prevent various security attacks, such as insider and ephemeral secret leakage (ESL) attacks, and fails to ensure perfect forward secrecy. To address these security weaknesses, we propose an anonymous and efficient authentication scheme with conditional privacy-preserving capabilities in IoV networks. The proposed scheme can ensure robustness against various security attacks and provide essential security features. The proposed scheme ensures conditional privacy to revoke malicious behavior in IoV networks. Moreover, our scheme uses only one-way hash functions and XOR operations, which are low-cost cryptographic operations suitable for IoV. We also prove the security of our scheme using the “Burrows–Abadi–Needham (BAN) logic”, “Real-or-Random (ROR) model”, and “Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool”. We evaluate and compare the performance and security features of the proposed scheme with existing methods. Consequently, our scheme provides improved security and efficiency and is suitable for practical IoV networks.

Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure. The collected data and learned knowledge can help the vehicular service provider (VSP) improve the global model accuracy, e.g., for road safety as well as better profits for both VSP and participating SVs. Nonetheless, there exist major challenges when implementing the FL in IoV networks, such as dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information history at each learning round. Then, each selected SV can collect on-road information and propose a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the VSP's limited payment budget and asymmetric information between the VSP and SVs. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster (even with only 10% of active SVs in the network) and obtain much higher social welfare of the network (up to 27.2 times) compared with those of other baseline FL methods.

All over the world, the increase in the use of transport systems is defined as the cause of traffic problems reflected mainly by the increase in the number of road accidents due to poor traffic management. In order to ensure an intelligent mobility and transport and thus in the trend of building smart cities, the interest has turned to the development of the internet of vehicles (IoV). The IoV communication network involves the evolution of vehicle connectivity enabling the exchange of real-time traffic data between vehicles, with their environment and everything related to it, through different network technologies. Given the complexity of the IoV, it is necessary that its environment is secure, reliable, and protected against attacks, and that it allows the diffusion of information throughout the network. The blockchain technology allows the securing of different data transactions exchanged between IoV nodes, given its provision of several cryptographic techniques and that it provides a distributed, transparent, and highly confidential database. In order to expand the area of the covered network, long-range (LoRa) designed for low power wide area networks (LPWANs) is used to ensure simultaneous and long range transmissions. This chapter presents an IoV-based architecture that integrates blockchain technology to cover the database security aspects and the LoRa network as a service for vehicle tracking that allows to collect information from different vehicles including location in order to be able to prevent the presence of road accidents with the aim of warning, alleviating traffic and minimizing the risk of having others. For its implementation, this system is based mainly on the measurement of the speed of vehicles to detect the deceleration or blockage of traffic in order to identify the presence of accidents.

Named Data Networking (NDN) is considered a future architecture for content distribution in the Internet of Vehicles (IoV). The primary principles of NDN, which include naming and in-network caching, are perfectly aligned with the IoV requirements for time and location independence. Despite significant research efforts, full-scale deployment remains limited due to ongoing concerns regarding trust, safety, and security within the IoV network. Moreover, traditional security algorithms proposed for IoV are complex, with high computational demands that challenge the strict real-time constraints. To minimize the computational overhead of vehicles, we proposed an RSU-empowered proxy signature scheme for NDN-based IoV. The security of the proposed scheme is proven to be Existentially Unforgeable against Adaptive Chosen-Message Attacks (EU-ACMA) under the Random Oracle Model (ROM), considering the hardness of the Hyperelliptic Curve Discrete Logarithm Problem (HCDLP). A performance analysis, which considers both computation time and communication overhead, shows that the proposed scheme effectively minimizes these factors. Besides, we applied the Multi-Criteria Decision-Making (MCDM) technique, namely Evaluation based on Distance from Average Solution (EDAS), to meet the particular need to prioritize data in IoV. The findings show that the proposed scheme performs better than those in the related literature.