FedHSQA: Robust Aggregation in Hierarchical Federated Learning via Anomaly Scoring-Based Adaptive Quantization for IoV

Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called the Blockchain Trust and Weighted Attribute-Based Access Control Strategy (BTWACS). First, we utilize both local and global blockchains to jointly maintain the generation, verification and storage of blocks, achieving distributed data storage and ensuring that data cannot arbitrarily be tampered with. Local blockchain mainly uses Road Side Unit (RSU) technology to calculate trust values, while global blockchain is mainly responsible for data storage and access policy selection. Secondly, we design a blockchain-based trust evaluation scheme called Blockchain-Based Trust Evaluation (BBTE). In this evaluation scheme, the trust value of the vehicle node is based on four factors: initial trust, historical experience trust, recommendation trust and RSU observation trust. CRITIC is used to determine the optimal weights of four factors to obtain the trust value. Then, we use the Network Simulator version 3 (NS3) to verify the security and accuracy of BBTE, improving the recognition accuracy and detection rate of malicious vehicle nodes. Finally, by mining the association relationships between attribute permissions among various roles, we construct a hierarchical access control strategy based on weight and trust, and further optimize the access strategy through pruning techniques. The experiment results indicate that this scheme can effectively respond to gray hole attacks, defamation attacks and collusion attacks from other vehicle nodes. This method can effectively reduce the computing and transmission costs of vehicles and meet the access requirements of multiple entities and roles in the IoV.

In the Internet of Vehicles (IoV), numerous potential applications have come up with the use of the Internet of Things (IoT)-empowered smart devices. In IoV, vehicles, roads, street signs and traffic lights can accordingly adjust to changing conditions in order to assist drivers, and also to improve safety, ease congestion and pollution reduction. Since various entities in an IoV environment make communications over public channels, there are potential security threats. To deal with such serious threats, we design a new blockchain-assisted certificateless key agreement protocol for IoV in smart transportation context, called Block-CLAP. In Block-CLAP, through authentication key management, traffic-centric data reach to a cluster head (CH) and then to its nearby road-side unit (RSU) securely using the established secret keys. A cloud server (CS) then securely collects the information from its attached RSUs and create the transactions. Later, the transactions are formed into blocks by the CS in a Peer-to-Peer (P2P) cloud servers network, and the blocks are verified and added through voting-based consensus algorithm in the blockchain. The detailed security analysis through formal, informal and formal security verification, and comparative study show that Block-CLAP provides superior security and has low communication and computational overheads as compared with other existing competing authentication schemes in the IoV environment. Finally, the blockchain-based implementation of Block-CLAP has been performed to measure computational time needed for a varied number of transactions per block and also for a varied number of blocks mined in the blockchain.

The current security method of Internet-of-Vehicles (IoV) systems is rare, which makes it vulnerable to various attacks. The malicious and unauthorized nodes can easily invade the IoV systems to destroy the integrity, availability, and confidentiality of information resources shared among vehicles. Indeed, access control mechanism can remedy this. However, as a static method, it cannot timely response to these attacks. To solve this problem, we propose an intelligent edge-chain-enabled access control framework with vehicle nodes and roadside units (RSUs) in this study. In our scenario, vehicle nodes act as lightweight nodes, whereas RUSs serve as full and edge nodes to provide access control services. Considering the low accuracy of risk prediction due to limited training sets, we leverage a generative adversarial networks (GANs) to convert the risk prediction to a sequence generation. Moreover, aiming at the problems of gradient disappearance and mode collapse existed in the original GANs, we devise a Wasserstein combined GANs (WCGANs). Simulation results demonstrate that WCGAN has higher prediction accuracy than the original GANs. Additionally, it can also improve the accuracy of access control of risk prediction-based access control (RPBAC) model.

The internet of vehicles (IoV), a variant of the traditional VANET, allows real-time data exchange between vehicles, roadside units, parking, and city infrastructure. Nevertheless, the IoV poses many security concerns due to its open nature. Traditional security solutions may not address all the IoV security risks and provide complete protection. Therefore, it is critical to establish trust and to identify dishonest nodes. As a result, trust management-based techniques are also required to improve IoV security. This paper proposes federated learning with a blockchain approach for trust management (FBTM) in IoV. Thus, a vehicular trust evaluation is designed to improve the data acquired for the federated learning model learning process. Moreover, a novel blockchain-based reputation system is developed to guarantee the storage and the share of global federated learning models. In the meanwhile, proof of reputation consensus is proposed to evaluate the roadside units operating as aggregators in the IoV network. Simulation results demonstrate that the proposed scheme is effective for IoV security.

An IoV enables secure transmission and storage of real-time traffic accident information among IoV devices like Vehicles, Road Side Unit (RSU), and Edge Servers to reduce unintended loss. Regardless of the advantages, accurate traffic accident prediction and decentralized data storage remain challenges for a reliable IoV system. We propose IntelligentChain, a blockchain and machine learning-based edge server for IoV that enables low-latency IoV device registration and secure traffic accident information communication between them. IntelligentChain then implements novel decentralized trust, consensus procedures for blockchain-based traffic accident information storage and predicts localized area traffic accidents using LSTM recurrent neural networks. IntelligentChain has been implemented on the Raspberry Pi 4. The experiment results illustrate that IntelligentChain achieves 90% prediction accuracy on the Kaggle dataset while decreasing transaction approval delay.

Internet of vehicles (IoV) promises to provide ubiquitous information exchange among moving vehicles and reliable connectivity to the internet. Therefore, IoV is becoming more and more popular as the number of connected vehicles is increasing. However, the existing vehicular communication infrastructure cannot guarantee reliable connectivity because all-time information exchange for every travelling vehicle is not assured due to lack of required number of roadside units (RSUs) especially along the intercity highways. This study is aimed towards exploring the use of cost-effective dynamic deployment of RSUs based upon the road traffic density and by ensuring the Line of Sight (LOS) among RSUs and Cellular Network Antennas. The unmanned aerial vehicles (UAVs) have a potential to serve as economical dynamic RSUs. Therefore, the use of UAVs along the roadside for providing reliable and ubiquitous information exchange among vehicles is proposed. The UAVs will be deployed along the roadside and their respective placement will be changed dynamically based upon the current traffic density in order to ensure the all-time connectivity with the travelling vehicles and the other UAVs/Cellular Network antennas. The reliability of the proposed network will be tested in terms of signal strength and packet delivery ratio (PDR) using the simulation.

Objectives: Traffic accidents cause severe social and economic impacts, demanding fast and reliable detection to minimize secondary collisions and improve emergency response. However, existing cloud-dependent detection systems often suffer from high latency and limited scalability, motivating the need for an edge-centric and consensus-free accident detection framework in IoV environments. Methods: This study presents a real-time accident detection framework tailored for Internet of Vehicles (IoV) environments. The proposed system forms an integrated IoV architecture combining on-vehicle inference, RSU-based validation, and asynchronous cloud reporting. The system integrates a lightweight ensemble of Vision Transformer (ViT) and EfficientNet models deployed on vehicle nodes to classify video frames. Accident alerts are generated only when both models agree (vehicle-level ensemble consensus), ensuring high precision. These alerts are transmitted to nearby Road Side Units (RSUs), which validate the events and broadcast safety messages without requiring inter-vehicle or inter-RSU consensus. Structured reports are also forwarded asynchronously to the cloud for long-term model retraining and risk analysis. Results: Evaluated on the CarCrash and CADP datasets, the framework achieves an F1-score of 0.96 with average decision latency below 60 ms, corresponding to an overall accuracy of 98.65% and demonstrating measurable improvement over single-model baselines. Conclusions: By combining on-vehicle inference, edge-based validation, and optional cloud integration, the proposed architecture offers both immediate responsiveness and adaptability, contrasting with traditional cloud-dependent approaches.

To enable intelligent transportation systems, the Internet of Vehicles (IoV) uses wireless communications between roadside units, onboard units), phones, and other gadgets within a vehicle to deliver a new convenient, comfortable, and safe driving experience. IoV is now more exposed and frequently targeted because of its greatly increased reliance on wireless communication, control, and computer technology, To put it another way, an adversary may target such an open channel to intercept, change, inject malicious messages, or erase loaded data. This could lead to replay, impersonation, middle attack, privileged insider, and other related attacks. An authentication protocol should strive to accomplish security, anonymity, and untraceability among other critical aspects. In this research, we suggest a brand-new key agreement and mutual authentication methodology for IoV powered Intelligent transportation system (ITS). We demonstrate that our suggested system is secure against several known threats in IoV- the enabled ITS environment. Furthermore, a thorough comparison of our proposed system to eight other competing ones reveals that it has the lowest communication and computational costs. In addition, it is more secure and provides more functionality characteristics.

The Internet of Vehicles (IoV) makes it possible to transmit information in real time between vehicles, providing a modern approach for autonomous driving, traffic safety, and other applications. Roadside units (RSUs) contribute to the enhancement of IoV's reliability and transmission efficiency, while mitigating the impact of low IoV penetration. The objective of RSU deployment optimization is to minimize the total cost with the premise of ensuring IoV reliability. We construct a distance-based reliability measure for IoV, which is expressed as the proportion of information transmitted in online mode to the total transmission distance. The distance distribution of the online and offline transmissions is computed using the information transmission model. A bi-objective optimization model is established with the objectives of minimizing the cost of RSU and maximizing the reliability of IoV. Meanwhile, based on variable probabilities of crossover and mutation, a nondomination level-based NSGA-II (NNSGA-II) is designed to improve the solving efficiency. Numerical results show the advantage of the proposed model over the models evaluated with the objective of reducing transmission time can be up to 18% in different traffic scenarios, and NNSGA-II is significantly more computationally efficient.

The Internet of Vehicles (IoV) has become a rapidly growing research area, with the USA, EU, and Japan leading the way. IoV safety applications rely on broadcasting messages to communicate with peers and infrastructure. However, these messages contain sensitive information that can be used by an adversary to track vehicles' movements or cause chaos if not properly protected, resulting in location privacy concerns. Despite various schemes being proposed to address this issue, a standard approach to providing proper privacy to IoV users has yet to be established. This article proposes a roadside unit (RSU)‐aided framework for pseudonym management to preserve location privacy in IoV by enabling anonymity. The RSUs function as key entities, collecting, distributing, and shuffling pseudonyms for use by vehicles to enforce anonymity. The proposed framework has been evaluated through experimental simulations on PREXT, demonstrating better performance than existing schemes in terms of privacy metrics, including traceability, anonymity set, confusion matrix, and maximum entropy. Realistic terrain has been taken into account, and the framework is designed to be flexible and adaptable for future needs.

Presenting better traffic management in urban scenarios has endured as a challenge to reduce the fatality rate. The model developed consolidates an evolving methodology to handle traffic on roads in an abstract way in internet of vehicles (IoV) orchestrated with unsupervised machine learning (USL) techniques. At first, the roads are sliced into segments with roadside units (RSU) that provide vehicle to everything (V2X) communication in a multi-hop manner and examine traffic in real-time. The unique nature of the proposed framework is to introduce USL into the RSU to learn about traffic patterns. The RSU upon learning the traffic patterns applies the Gaussian mixture model (GMM) to observe the variation in the traffic pattern to immediately generate warning alerts and collision forward messages to the vehicles on road. The application of USL and GMM ensures speed control of vehicles with traffic alerts, thereby deteriorating the fatality rates.

The Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security. A variety of emerging applications/services bring explosively growing demands for mobile data traffic between connected vehicles and roadside units (RSUs), imposing the significant challenge of spectrum scarcity in the IoV. In this article, we propose a cooperative semantic-aware architecture to convey essential semantics from collaborated users to servers for lessening data traffic. In contrast to current solutions that are mainly based on piling up highly complex signal processing techniques and multiple access capabilities in terms of syntactic communications, this article puts forth the idea of semantic-aware content delivery in the IoV. Specifically, the successful transmission of essential semantics of the source data is pursued rather than the accurate reception of symbols regardless of their meaning, as in conventional syntactic communications. To assess the benefits of the proposed architecture, we provide a case study of an image retrieval task for vehicles in intelligent transportation systems (ITSs). Simulation results demonstrate that the proposed architecture outperforms existing solutions, with fewer radio resources, especially in a low-signal-to-noise-ratio (SNR) regime, which can shed light on the potential of the proposed architecture in extending applications in extreme environments.

Recent developments in IoT have facilitated advancements in the Internet of Vehicles (IoV) with autonomous vehicles and roadside infrastructure as its key components. IoV aims to provide new innovative services for different modes of transport with adaptive traffic management and enables vehicles to broadcast messages to improve traffic safety and efficiency. However, due to nontrusted environments, it is difficult for vehicles to evaluate the credibility of the messages that they receive. Therefore, trust establishment in IoV is a key security concern that is constantly limited by scalability challenges. This article proposes a blockchain-based protocol for IoV using smart contracts, physical unclonable functions (PUFs), certificates, and a dynamic Proof-of-Work (dPoW) consensus algorithm. The blockchain, in conjunction with contracts, provides a secure framework for registering trusted vehicles and blocking malicious ones. PUFs are used to assign a unique identity to each vehicle via which trust is established. Certificates are issued by roadside units that preserve the privacy of vehicles, whereas the dPoW consensus allows the protocol to scale according to the incoming traffic generated by the vehicles. To demonstrate the feasibility and scalability of the proposed protocol, security and performance analyses are presented. A case study is also discussed along with a comparative analysis, which confirms that our protocol can provide superior decentralized trust management for IoV.

With the frequent occurrence of network security incidents in recent years, it has become very important to detect anomalous behaviour in networks as early and accurately as possible. Anomaly detection can improve the security of complex network systems by detecting abnormal and unreliable nodes, and thus it has become a hot research direction that has attracted wide attention. At present, abstracting real complex systems into complex networks for anomaly detection is the mainstream research method. However, the existing methods still have challenges in extracting network heterogeneity information and attribute information, so we propose a multi-view based anomaly detection method for heterogeneous attributed networks, MVAD HAN. This method can better extract the heterogeneous structural information and rich attribute information of the network to model heterogeneous attributed networks. Our method adopts an encoder-decoder architecture. First, in the encoder part, we use the Heterogeneous Graph Transformer with multiple views to learn node embeddings that fuse the heterogeneous information of the network. In the decoder part, we use an inner product decoder to reconstruct the network topology, a multilayer perceptron-based decoder to better reconstruct the network attribute information, and a linear projection to reconstruct the node type information of the network. Finally, we compute an anomaly score for each node using three reconstruction errors: network structure, attributes and node type. The higher the reconstruction error of a node, the higher the anomaly score and the higher the probability of an anomaly. Finally, anomalous nodes are identified by ranking the anomaly scores and setting a threshold. We validate the effectiveness of the proposed method on four real-world datasets. The experimental results show that this method outperforms several of the baseline methods and has a good performance in anomaly detection.

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.