Search Results (277)

Due to technological advancement, the advent of smart cities has facilitated the deployment of advanced urban management systems. This integration has been made possible through the Internet of Vehicles (IoV), a foundational technology. By connecting smart cities with vehicles, the IoV enhances the safety and efficiency of transportation. This interconnected system facilitates wireless communication among vehicles, enabling the exchange of crucial traffic information. However, this significant technological advancement also raises concerns regarding privacy for individual users. This paper presents an innovative privacy-preserving authentication scheme focusing on IoV using physical unclonable functions (PUFs). This scheme employs the k-nearest neighbor (KNN) encryption technique, which possesses a multi-multi searching property. The main objective of this scheme is to authenticate autonomous vehicles (AVs) within the IoV framework. An innovative PUF design is applied to generate random keys for our authentication scheme to enhance security. This two-layer security approach protects against various cyber-attacks, including fraudulent identities, man-in-the-middle attacks, and unauthorized access to individual user information. Due to the substantial amount of information that needs to be processed for authentication purposes, our scheme is implemented using hardware acceleration on an Nexys A7-100T FPGA board. Our analysis of privacy and security illustrates the effective accomplishment of specified design goals. Furthermore, the performance analysis reveals that our approach imposes a minimal communication and computational burden and optimally utilizes hardware resources to accomplish design objectives. The results show that the proposed authentication scheme exhibits a non-linear increase in encryption time with a growing AV ID size, starting at 5$\mathsf{\mu }$s for 100 bits and rising to 39 $\mathsf{\mu }$s for 800 bits. Also, the result demonstrates a more gradual, linear increase in the search time with a growing AV ID size, starting at less than 1 $\mathsf{\mu }$s for 100 bits and rising to less than 8 $\mathsf{\mu }$s for 800 bits. Additionally, for hardware utilization, our scheme uses only 25% from DSP slides and IO pins, 22.2% from BRAM, 5.6% from flip-flops, and 24.3% from LUTs. Full article

The emergence of 6G-enabled Internet of Vehicles (IoV) promises to revolutionize mobility and connectivity, integrating vehicles into a mobile Internet of Things (IoT)-oriented wireless sensor network (WSN). Meanwhile, 5G technologies and mobile edge computing further support this vision by facilitating real-time connectivity and empowering massive access to the Internet. Within this context, IoT-oriented WSNs play a crucial role in intelligent transportation systems, offering affordable alternatives for traffic monitoring and management. Efficient sensor selection thus represents a critical concern while deploying WSNs on urban networks. In this paper, we provide an overview of such a notably hard problem. The contribution is twofold: (i) surveying state-of-the-art model-based techniques for efficient sensor selection in traffic flow monitoring, emphasizing challenges of sensor placement, and (ii) advocating for the development of data-driven methodologies to enhance sensor deployment efficacy and traffic modeling accuracy. Further considerations underscore the importance of data-driven approaches for adaptive transportation systems aligned with the IoV paradigm. Full article

This study examines the integration of the Internet of Vehicles (IoV) and blockchain as tools to optimize traffic management in Roll-on/Roll-off (Ro-Ro) terminals within the Spanish port system. Faced with increasing operational complexity, these technologies present innovative solutions to enhance efficiency, reduce waiting times, and ensure transparency in data management. The methodology follows five main phases: analysis of the current scenario, establishment of a baseline, calculations to evaluate key outcomes, analysis of blockchain implementation, and discussion of results. Key variables include waiting times, vehicular flow, CO₂ emissions, and operational costs, comparing manual and automated scenarios. The findings reveal that the combined use of IoV and blockchain can increase vehicular flow by up to 70%, reduce waiting times by 2.56 min, and decrease CO₂ emissions by 57.74 kg per hour. Additionally, automation significantly reduces operational costs, yielding average savings of over EUR 500 per hour. This study concludes that adopting these technologies transforms port operations by fostering sustainability, efficiency, and safety. However, challenges remain, including high initial implementation costs and system interoperability issues. This work underscores the need for strategic approaches to overcome these barriers and positions Spanish ports as potential leaders in logistics innovation, aligning with global demands for sustainable, efficient, and transparent port operations. Full article

Internet of Vehicles (IoV) is a concept that combines IoT and vehicular ad hoc networks. In IoV environments, vehicles constantly move and communicate with other roadside units (edge servers). Due to the vehicles’ insufficient computing power, repetitive authentication procedures can be burdensome for automobiles. In recent years, numerous authentication protocols for IoV environments have been proposed. However, there is no study that considers both re-authentication and handover authentication situations, which are essential for seamless communication in vehicular networks. In this study, we propose a chaotic map-based seamless authentication scheme for edge-assisted IoV environments. We propose authentication protocols for initial, handover, and re-authentication situations and analyze the security of our scheme using informal methods, the real-or-random (RoR) model, and the Scyther tool. We also compare the proposed scheme with existing schemes and show that our scheme has superior performance and provides more security features. To our knowledge, This paper is the first attempt to design an authentication scheme considering both handover and re-authentication in the IoV environment. Full article

The traditional consensus algorithm based on the Internet of Vehicles (IoV) system has the disadvantages of high latency, low reliability, and weak fault tolerance, and it cannot make real-time adjustments according to the actual environment, making the system vulnerable to malicious control, inefficiency, and poor environmental adaptability. To solve this problem, we propose a gradually accelerating environment adaptive consensus algorithm, AE-PBFT, that can be applied to IoV. It includes a trust management model that achieves gradual acceleration by recording the historical continuous behavior of nodes, thereby improving the efficiency of screening nodes with different intentions, accelerating the consensus process, and reducing latency. At the same time, we introduce a dynamic consensus group division mechanism based on environmental adaptive changes, which can adaptively adjust the number of nodes participating in the consensus process according to the needs of the operating environment, to deal with extreme situations, thereby improving the reliability and fault tolerance of the system. Experiments confirm that the performance of our proposed solution is superior to current solutions in terms of consensus latency and fault tolerance and is more suitable for the operating environment of IoV. Full article

The rapid growth of the Internet of Vehicles (IoV) requires secure, efficient, and reliable data exchanges among multiple stakeholders. Traditional centralized database systems can hardly address the challenges associated with data privacy, integrity, and scalability in this decentralized ecosystem. In this paper, we propose a Hyperledger Fabric-Based Multi-Channel Structure to overcome these limitations. By leveraging the blockchain architecture, the system ensures data confidentiality and integrity by segregating data into exclusive channels and enabling different organizations to collaborate. Cross-channel communication ensures security when data are interacted with. Chaincodes automate transactions and enhance trust between participants. Our functional tests and performance tests by using Hyperledger Caliper verified the effectiveness of the system in real-world scenarios, highlighting its advantages over traditional systems in terms of decentralization, transparency, and security. Future work will focus on enhancing the user experience and integrating the system with edge computing. Eventually, attempts will be made to operationalize it in real-world environments. Full article

With the rapid development of the Internet of Vehicles (IoV) and autonomous driving technologies, robust and accurate visual pose perception has become critical for enabling smart connected vehicles. Traditional deep learning-based localization methods face persistent challenges in real-world vehicular environments, including occlusion, lighting variations, and the prohibitive cost of collecting diverse real-world datasets. To address these limitations, this study introduces a novel approach by combining Vision-LSTM (ViL) with synthetic image data generated from high-fidelity 3D models. Unlike traditional methods reliant on costly and labor-intensive real-world data, synthetic datasets enable controlled, scalable, and efficient training under diverse environmental conditions. Vision-LSTM enhances feature extraction and classification performance through its matrix-based mLSTM modules and advanced feature aggregation strategy, effectively capturing both global and local information. Experimental evaluations in independent target scenes with distinct features and structured indoor environments demonstrate significant performance gains, achieving matching accuracies of 91.25% and 95.87%, respectively, and outperforming state-of-the-art models. These findings underscore the innovative advantages of integrating Vision-LSTM with synthetic data, highlighting its potential to overcome real-world limitations, reduce costs, and enhance accuracy and reliability for connected vehicle applications such as autonomous navigation and environmental perception. Full article

Traffic congestion imposes burdens on society and individuals. In 2022, the average congestion cost per auto commuter in the USA was USD1259. New possibilities to increase traffic efficiency are now available as vehicles can interact using Vehicular Ad Hoc Network (VANET) systems, a subset of the Internet of Vehicles (IoV). The traffic assignment problem deals with road network traffic optimization. It is a complex and challenging problem. A few solutions incorporating VANET technology have been presented; most are centralized or depend on infrastructure. In previous work, we introduced Road-ACO, an ant colony optimization (ACO), single-hop, decentralized, infrastructure-less, VANET solution. In this paper, we propose a new multi-hop-enabled, decentralized, ant-colony-inspired algorithm for dynamic highway traffic assignment. The algorithm works for large road networks and requires no infrastructure. We develop Veins framework-based simulations to evaluate the algorithm’s performance. The results indicate that the proposed algorithm consistently outperforms Road-ACO and performs optimally on road segments up to 4000 m long, with improvements of up to 40% on average travel time. Full article

Integrated Sensing, Communication, and Computation (ISCC) has become a key technology driving the development of the Internet of Vehicles (IoV) by enabling real-time environmental sensing, low-latency communication, and collaborative computing. However, the increasing sensing data within the IoV leads to demands of fast data transmission in the context of limited communication resources. To address this issue, we propose a Collaborative Sensing-Aware Task Offloading (CSTO) mechanism for ISCC to reduce the sensing tasks transmission delay. We formulate a joint task offloading and communication resource allocation optimization problem to minimize the total processing delay of all vehicular sensing tasks. To solve this mixed-integer nonlinear programming (MINLP) problem, we design a two-stage iterative optimization algorithm that decomposes the original optimization problem into a task offloading subproblem and a resource allocation subproblem, which are solved iteratively. In the first stage, a Deep Reinforcement Learning algorithm is used to determine task offloading decisions based on the initial setting. In the second stage, a convex optimization algorithm is employed to allocate communication bandwidth according to the current task offloading decisions. We conduct simulation experiments by varying different crucial parameters, and the results demonstrate the superiority of our scheme over other benchmark schemes. Full article

Future autonomous vehicles will interact with traffic infrastructure through roadside units (RSUs) directly connected to transportation field cabinets (TFCs). These TFCs must be interconnected to share traffic information, enabling infrastructure-to-infrastructure (I2I) communications that are reliable, synchronous and capable of transmitting vehicle data to the Internet. However, I2I communications present a complex optimization challenge. This study addresses this by proposing the design, implementation, and evaluation of an automated management model for I2I service channels based on multi-agent reinforcement learning (MARL) integrated with deep reinforcement learning (DRL). The proposed models efficiently manage the routing and scheduling of data frames between internet of vehicles (IoV) infrastructure devices through time-sensitive networking (TSN) to ensure real-time synchronous I2I communications. The solution incorporates both a routing model and a scheduling model, evaluated in a simulated shared environment where agents operate within the TSN control plane. Both models are tested for different topologies and background traffic levels. The results demonstrate that the models establish the majority of paths in the scenario, adhering to near-optimal routing and scheduling policies. Recursively, for each individual request to create a service channel, the system establishes online an optimal synchronous path between entities with a limited time budget. In total, 71% of optimal routing paths are established and 97% of optimal schedules are achieved. The approach takes into account the periodic nature of the transmitted data and its robustness through TSN networks, obtaining 99 percent of compliant service requests with flow jitter levels below 100 microseconds for different topologies and different network utility percentages. The proposed solution achieves lower execution delays compared to the iterative ILP approach. Additionally, the solution facilitates the integration of 5G networks for vehicle-to-infrastructure (V2I) communications, which is identified as an area for future exploration. Full article

In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management. Full article

With the rapid development of in-vehicle communication technology, the Internet of Vehicles (IoV) is gradually becoming a core component of next-generation transportation networks. However, ensuring the activity and reliability of IoV nodes remains a critical challenge. The emergence of blockchain technology offers new solutions to the problem of node selection in IoV. Nevertheless, traditional blockchain networks may suffer from malicious nodes, which pose security threats and disrupt normal blockchain operations. To address the issues of low participation and security risks among IoV nodes, this paper proposes a federated learning (FL) scheme based on blockchain and reputation value changes. This scheme encourages active involvement in blockchain consensus and facilitates the selection of trustworthy and reliable IoV nodes. First, we avoid conflicts between computing power for training and consensus by constructing state-channel transitions to move training tasks off-chain. Task rewards are then distributed to participating miner nodes based on their contributions to the FL model. Second, a reputation mechanism is designed to measure the reliability of participating nodes in FL, and a Proof of Contribution Consensus (PoCC) algorithm is proposed to allocate node incentives and package blockchain transactions. Finally, experimental results demonstrate that the proposed incentive mechanism enhances node participation in training and successfully identifies trustworthy nodes. Full article

The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional limitations in system capacity, precipitate significant challenges in allocating wireless resources for vehicular networks. In addressing these challenges, this study formulates the resource allocation issue as a multi-agent deep reinforcement learning scenario and introduces a novel multi-agent actor-critic framework. This framework incorporates a prioritized experience replay mechanism focused on distributed execution, which facilitates decentralized computing by structuring the training processes and defining specific reward functions, thus optimizing resource allocation. Furthermore, the framework prioritizes empirical data during the training phase based on the temporal difference error (TD error), selectively updating the network with high-priority data at each sampling point. This strategy not only accelerates model convergence but also enhances the learning efficacy. The empirical validations confirm that our algorithm augments the total capacity of vehicle-to-infrastructure (V2I) links by 9.36% and the success rate of vehicle-to-vehicle (V2V) transmissions by 6.74% compared with a benchmark algorithm. Full article

With the increasing connectivity and automation on the Internet of Vehicles, safety, security, and privacy have become stringent challenges. In the last decade, several cryptography-based protocols have been proposed as intuitive solutions to protect vehicles from information leakage and intrusions. Before generating the encryption keys, a random number generator (RNG) plays an important component in cybersecurity. Several deep learning-based RNGs have been deployed to train the initial value and generate pseudo-random numbers. However, interference from actual unpredictable driving environments renders the system unreliable for its low-randomness outputs. Furthermore, dynamics in the training process make these methods subject to training instability and pattern collapse by overfitting. In this paper, we propose an Effective Pseudo-Random Number Generator (EPRNG) which exploits a deep convolution generative adversarial network (DCGAN)-based approach using our processed vehicle datasets and entropy-driven stopping method-based training processes for the generation of pseudo-random numbers. Our model starts from the vehicle data source to stitch images and add noise to enhance the entropy of the images and then inputs them into our network. In addition, we design an entropy-driven stopping method that enables our model training to stop at the optimal epoch so as to prevent overfitting. The results of the evaluation indicate that our entropy-driven stopping method can effectively generate pseudo-random numbers in a DCGAN. Our numerical experiments on famous test suites (NIST, ENT) demonstrate the effectiveness of the developed approach in high-quality random number generation for the IoV. Furthermore, the PRNGs are successfully applied to image encryption, and the performance metrics of the encryption are close to ideal values. Full article

The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks. The ECAUP meets the unique mobility and security demands of IoV by enabling fine-grained access control and dynamic key updates for RSUs through a factorial tree structure, ensuring both forward and backward secrecy. Additionally, physical unclonable functions (PUFs) are utilized to provide end-to-end authentication and physical layer security, further enhancing the system’s resilience against sophisticated cyber-attacks. The security of the ECAUP is formally verified using BAN Logic and ProVerif, and a comparative analysis demonstrates its superiority in terms of overhead efficiency (more than 50%) and security features over existing protocols. This work contributes to the development of secure, resilient, and efficient intelligent transportation systems, ensuring robust communication and protection in sensor-based IoV environments. Full article