Search Results (258)

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. Full article

The rapid evolution of smart cities relies heavily on advancements in wireless communication systems and extensive IoT networks. This paper offers a comprehensive review of the critical role and future potential of integrating unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance Internet of Vehicles (IoV) systems within beyond-fifth-generation (B5G) and sixth-generation (6G) networks. We explore the combination of quasi-optical millimeter-wave (mmWave) signals with UAV-enabled, RIS-assisted networks and their applications in urban environments. This review covers essential areas such as channel modeling and position-aware beamforming in dynamic networks, including UAVs and IoVs. Moreover, we investigate UAV navigation and control, emphasizing the development of obstacle-free trajectory designs in dense urban areas while meeting kinodynamic and motion constraints. The emerging potential of RIS-equipped UAVs (RISeUAVs) is highlighted, along with their role in supporting IoVs and in mobile edge computing. Optimization techniques, including convex programming methods and machine learning, are explored to tackle complex challenges, with an emphasis on studying computational complexity and feasibility for real-time operations. Additionally, this review highlights the integrated localization and communication strategies to enhance UAV and autonomous ground vehicle operations. This tutorial-style overview offers insights into the technical challenges and innovative solutions of the next-generation wireless networks in smart cities, with a focus on vehicular communications. Finally, future research directions are outlined. Full article

The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing (VEC) systems, which considers the high mobility of vehicles and the limited coverage and computational capacities of drones. We introduce the Mobility-Aware Vehicular Task Offloading (MAVTO) algorithm, designed to optimize task offloading decisions, manage resource allocation, and predict vehicle positions for seamless offloading. MAVTO leverages container-based virtualization for efficient computation, offering flexibility in resource allocation in multiple offload modes: direct, predictive, and hybrid. Extensive experiments using real-world vehicular data demonstrate that the MAVTO algorithm significantly outperforms other methods in terms of task completion success rate, especially under varying task data volumes and deadlines. Full article

Reconfigurable intelligent surfaces (RISs), as an emerging radio technology, are widely used to expand the transmission distance and structure cascade channels to improve the performance of communication systems. However, based on the continuous development of wireless communication technology, as Internet of Vehicles (IoV) communication systems assisted with RIS and non-orthogonal multiple access (NOMA) can improve the overall transmission rate and system performance, the physical layer security (PLS) issue has gradually attracted attention and has become more and more important in the application of the system. In this paper, our aim is to investigate the potential threats for PLS, where an RIS is utilized in order to improve the security of wireless communications. In particular, we consider the non-fixed RIS location and wiretapping behavior of eavesdroppers on the data in this work, and further analyze the maximum safe-rate for above location assumptions. Numerical results reveal that RIS provides significant advantages on security performance, as well as providing a useful reference for the security design of future wireless communication systems, which verify the correctness of our analysis and the effectiveness of the proposed scheme. Full article

The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework. Full article

Recently, with a crucial role in developing smart transportation systems, the Internet of Vehicles (IoV), with all kinds of in-vehicle devices, has undergone significant advancement for autonomous driving, in-vehicle infotainment, etc. With the development of these IoV devices, the complexity and volume of in-vehicle data flows within information communication have increased dramatically. To adapt these changes to secure and smart transportation, encrypted communication realization, real-time decision-making, traffic management enhancement, and overall transportation efficiency improvement are essential. However, the security of a traffic system under encrypted communication is still inadequate, as attackers can identify in-vehicle devices through fingerprinting attacks, causing potential privacy breaches. Nevertheless, existing IoV traffic application models for encrypted traffic identification are weak and often exhibit poor generalization in some dynamic scenarios, where route switching and TCP congestion occur frequently. In this paper, we propose LineGraph-GraphSAGE (L-GraphSAGE), a graph neural network (GNN) model designed to improve the generalization ability of the IoV application of traffic identification in these dynamic scenarios. L-GraphSAGE utilizes node features, including text attributes, node context information, and node degree, to learn hyperparameters that can be transferred to unknown nodes. Our model demonstrates promising results in both UNSW Sydney public datasets and real-world environments. In public IoV datasets, we achieve an accuracy of 94.23%(↑0.23%). Furthermore, our model achieves an F1 change rate of 0.20%(↑96.92%) in $\alpha$ train, $\beta$ infer, and 0.60%(↑75.00%) in $\beta$ train, $\alpha$ infer when evaluated on a dataset consisting of five classes of data collected from real-world environments. These results highlight the effectiveness of our proposed approach in enhancing IoV application identification in dynamic network scenarios. Full article

The rapid advancement and adoption of electric vehicles (EVs) necessitate innovative solutions to address integration challenges in modern charging infrastructure. Dynamic wireless charging (DWC) is an innovative solution for powering electric vehicles (EVs) using multiple magnetic transmitters installed beneath the road and a receiver located on the underside of the EV. Dynamic charging offers a solution to the issue of range anxiety by allowing EVs to charge while in motion, thereby reducing the need for frequent stops. This manuscript reviews several pivotal areas critical to the future of EV DWC technology such as authentication techniques, blockchain applications, driver identification systems, economic aspects, and emerging communication technologies. Ensuring secure access to this charging infrastructure requires fast, lightweight authentication systems. Similarly, blockchain technology plays a critical role in enhancing the Internet of Vehicles (IoV) architecture by decentralizing and securing vehicular networks, thus improving privacy, security, and efficiency. Driver identification systems, crucial for EV safety and comfort, are analyzed. Additionally, the economic feasibility and impact of DWC are evaluated, providing essential insights into its potential effects on the EV ecosystem. The paper also emphasizes the need for quick and lightweight authentication systems to ensure secure access to DWC infrastructure and discusses how blockchain technology enhances the efficiency, security, and privacy of IoV networks. The importance of driver identification systems for comfort and safety is evaluated, and an economic study confirms the viability and potential benefits of DWC for the EV ecosystem. Full article

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder–noise–decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks. Full article

With the help of traffic lights and street cameras, optical camera communication (OCC) can be adopted in Internet of Vehicles (IoV) applications to realize communication between vehicles and roadside units. However, the encoded light emitted by these OCC transmitters (LED infrastructures on the roadside and/or LED-based headlamps embedded in cars) will generate stripe patterns in image frames captured by existing license-plate recognition systems, which seriously degrades the accuracy of the recognition. To this end, we propose and experimentally demonstrate a method that can reduce the interference of OCC stripes in the image frames captured by the license-plate recognition system. We introduce an innovative pipeline with an end-to-end image reconstruction module. This module learns the distribution of images without OCC stripes and provides high-quality license-plate images for recognition in OCC conditions. In order to solve the problem of insufficient data, we model the OCC strips as multiplicative noise and propose a method to synthesize a pairwise dataset under OCC using the existing license-plate dataset. Moreover, we also build a prototype to simulate real scenes of the OCC-based vehicle networks and collect data in such scenes. Overall, the proposed method can achieve a recognition performance of $81.58$% and $79.35$% on the synthesized dataset and that captured from real scenes, respectively, which is improved by about $31.18$% and $24.26$%, respectively, compared with the conventional method. Full article

Unmanned aerial vehicles (UAVs) are utilized for wireless communication support of Internet of Intelligent Vehicles (IoVs). Intelligent vehicles (IVs) need vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) wireless communication for real-time perception knowledge exchange and dynamic environment modeling for safe autonomous driving and mission accomplishment. UAVs autonomously navigate through dense urban areas to provide aerial line-of-sight (LoS) communication links for IoVs. Real-time UAV trajectory design is required for minimum energy consumption and maximum channel performance. However, this is multidisciplinary research including (1) dynamic-aware kinematic (kinodynamic) planning by considering UAVs’ motion and nonholonomic constraints; (2) channel modeling and channel performance improvement in future wireless networks (i.e., beyond 5G and 6G) that are limited to beamforming to LoS links with the aid of reconfigurable intelligent surfaces (RISs); and (3) real-time obstacle-free crash avoidance 3D trajectory optimization in dense urban areas by modeling obstacles and LoS paths in convex programming. Modeling and solving this multilateral problem in real-time are computationally prohibitive unless extensive computational and overhead processing costs are imposed. To pave the path for computationally efficient yet feasible real-time trajectory optimization, this paper presents UAV kinodynamic modeling. Then, it proposes a convex trajectory optimization problem with the developed linear kinodynamic models. The optimality and smoothness of the trajectory optimization problem are improved by utilizing model predictive control and quadratic state feedback control. Simulation results are provided to validate the methodology. Full article

The current Internet of Vehicles (IoV) faces significant challenges related to resource heterogeneity, which adversely impacts the convergence speed and accuracy of federated learning models. Existing studies have not adequately addressed the problem of resource-constrained vehicles that slow down the federated learning process particularly under conditions of high mobility. To tackle this issue, we propose a model partition collaborative training mechanism that decomposes training tasks for resource-constrained vehicles while retaining the original data locally. By offloading complex computational tasks to nearby service vehicles, this approach effectively accelerates the slow training speed of resource-limited vehicles. Additionally, we introduce an optimal matching method for collaborative service vehicles. By analyzing common paths and time delays, we match service vehicles with similar routes and superior performance within mobile service vehicle clusters to provide effective collaborative training services. This method maximizes training efficiency and mitigates the negative effects of vehicle mobility on collaborative training. Simulation experiments demonstrate that compared to benchmark methods, our approach reduces the impact of mobility on collaboration, achieving large improvements in the training speed and the convergence time of federated learning. Full article

The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this transformation, leading to complex vehicular networks that are crucial yet susceptible to cyber threats. The complexity and openness of these networks expose them to a plethora of cyber-attacks, from passive eavesdropping to active disruptions like Denial of Service and Sybil attacks. These not only compromise the safety and efficiency of vehicular networks but also pose a significant risk to the stability and resilience of the Internet of Vehicles. Addressing these vulnerabilities, this paper proposes a Dynamic Forest-Structured Ensemble Network (DFSENet) specifically tailored for the Internet of Vehicles (IoV). By leveraging data-balancing techniques and dimensionality reduction, the DFSENet model is designed to detect a wide range of cyber threats effectively. The proposed model demonstrates high efficacy, with an accuracy of 99.2% on the CICIDS dataset and 98% on the car-hacking dataset. The precision, recall, and f-measure metrics stand at 95.6%, 98.8%, and 96.9%, respectively, establishing the DFSENet model as a robust solution for securing the IoV against cyber-attacks. Full article

With the rapid development of the Internet of Vehicles (IoV), the demand for secure and efficient signature verification is becoming increasingly urgent. To meet this need, we propose an efficient SM9 aggregate signature scheme implemented on Field-Programmable Gate Array (FPGA). The scheme includes both fault-tolerant and non-fault-tolerant aggregate signature modes, which are designed to address challenges in various network environments. We provide security proofs for these two signature verification modes based on a K-ary Computational Additive Diffie–Hellman (K-CAA) difficult problem. To handle the numerous parallelizable elliptic curve point multiplication operations required during verification, we utilize FPGA’s parallel processing capabilities to design an efficient parallel point multiplication architecture. By the Montgomery point multiplication algorithm and the Barrett modular reduction algorithm, we optimize the single-point multiplication computation unit, achieving a point multiplication speed of 70776 times per second. Finally, the overall scheme was simulated and analyzed on an FPGA platform. The experimental results and analysis indicate that under error-free conditions, the proposed non-fault-tolerant aggregate mode reduces the verification time by up to 97.1% compared to other schemes. In fault-tolerant conditions, the proposed fault-tolerant aggregate mode reduces the verification time by up to 77.2% compared to other schemes. When compared to other fault-tolerant aggregate schemes, its verification time is only 28.9% of their consumption, and even in the non-fault-tolerant aggregate mode, the verification time is reduced by at least 39.1%. Therefore, the proposed scheme demonstrates significant advantages in both error-free and fault-tolerant scenarios. Full article

Internet of Vehicles applications are known to be critical and time-sensitive. The value proposition of edge computing comprises its lower latency, advantageous bandwidth consumption, privacy, management, efficiency of treatments, and mobility, which aim to improve vehicular and traffic services. Successful stories have been observed between IoV and edge computing to support smooth mobility and the use of local resources. However, vehicle travel, especially due to high-speed movement and intersections, can result in IoV devices losing connection and/or processing with high latency. This paper proposes a Cluster Collaboration Vehicular Edge Computing (CCVEC) framework that aims to guarantee and enhance the connectivity between vehicle sensors and the cloud by utilizing the edge computing paradigm in the middle. The objectives are achieved by utilizing the cluster management strategies deployed between cloud and edge computing servers. The framework is implemented in OpenStack cloud servers and evaluated by measuring the throughput, latency, and memory parameters in two different scenarios. The results obtained show promising indications in terms of latency (approximately 390 ms of the ideal status) and throughput (30 kB/s) values, and thus appears acceptable in terms of performance as well as memory. Full article

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network. Full article