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The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%. [ABSTRACT FROM AUTHOR]

Connected components in automobiles face cybersecurity threats if not adequately protected. The Controller Area Network (CAN), the primary communication protocol in vehicular systems, lacks essential security mechanisms, necessitating heightened cybersecurity awareness in the automotive industry. This paper presents a methodology to enhance cybersecurity within the CAN network, including a comparative study with benchmark results. Our research focuses on improving multiclass Intrusion Detection Systems (IDSs) for the CAN network. We introduce a hybrid deep learning model tailored for precise multiclass classification of attacks, an Artificial Neural Network (ANN)-based Light Gradient Boosting Machine (LightGBM) IDS that incorporates a comprehensive set of features. The evaluation on diverse cyber-attacks from the Car-Hacking dataset demonstrates the model's effectiveness. This study contributes to the field by offering a brief assessment of current state-of-the-art methods and presenting a novel approach to bolster cybersecurity. The proposed IDS serves as a valuable reference for ensuring the cybersecurity of automotive systems. [ABSTRACT FROM AUTHOR]

The study focuses on utilizing the computational resources present in vehicles to enhance the performance of multi-access edge computing (MEC) systems. While vehicles are typically equipped with computational services for vehicle-centric Internet of Vehicles (IoV) applications, their resources can also be leveraged to reduce the workload on edge servers and improve task processing speed in MEC scenarios. Previous research efforts have overlooked the potential resource utilization of passing vehicles, which can be a valuable addition to MEC systems alongside parked cars. This study introduces an assisted MEC scenario where a base station (BS) with an edge server serves various devices, parked cars, and vehicular traffic. A cooperative approach using the Deep Deterministic Policy Gradient (DDPG) based Federated Learning method is proposed to optimize resource allocation and job offloading. This method enables the transfer of device operations from devices to the BS or from the BS to vehicles based on specific requirements. The proposed system also considers the duration for which a vehicle can provide job offloading services within the range of the BS before leaving. The objective of the DDPG-FL method is to minimize the overall priority-weighted task computation time. Through simulation results and a comparison with three other schemes, the study demonstrates the superiority of their proposed method in seven different scenarios. The findings highlight the potential of incorporating vehicular resources in MEC systems, showcasing improved task processing efficiency and overall system performance. [ABSTRACT FROM AUTHOR]

The fast growth of internet of vehicles (IoV) has created a new area of connectedness, with promising safety and efficiency in transportation. However, this advancement in vehicle technology has come with significant cybersecurity risks, specifically through control area network (CAN) protocol and other communication techniques within vehicles. This experimental study suggests a machine learning (ML) based security approach based on the extreme learning machine (ELM) algorithm to address these challenges. Unlike customary neural networks, ELM is known for its fast processing, minimal training time, and high accuracy, making it preferably suitable for dynamic IoV environments. The methodology involves data preprocessing, feature selection, and employing ELM for attack classification; the algorithm’s performance is evaluated using CARHacking, NSL-KDD, and EdgeIIoT datasets. We also examine the significance of distributed processing to enhance the computational efficiency of the model, obtaining 89% accuracy in 3 ms run-time for external networks, and 83% accuracy with 9 ms run-time for intra-vehical networks. This newly proposed security mechanism using ELM shows very accurate results in detecting intrusions with a high recall rate and reduced computation time through distributed processing. [ABSTRACT FROM AUTHOR]

In the contemporary era, the Internet of Vehicles (IoV) plays a pivotal role in traffic management, especially in the context of highly scalable and dynamic 5 G networks. Safeguarding these networks presents numerous challenges, particularly in controlling access for unauthenticated users and establishing secure key agreements with fine-grained access control. Balancing these security measures is vital to prevent unauthorized information flow while aligning with the promising goals of future generation technologies. Despite numerous related studies being conducted, existing schemes face risks such as privacy breaches, identity tracing, and substantial computation overheads. Consequently, there is an urgent need to introduce a more secure and efficient scheme to counter various attacks. This paper introduces an authenticated key agreement scheme, tailored for fog-based IoV. The scheme's security is established under the Random Oracle Model and verified using the ProVerif tool. Additionally, a performance assessment is conducted, demonstrating that the proposed protocol simultaneously meets both security and efficiency requirements when compared to several related schemes. [ABSTRACT FROM AUTHOR]

Vehicle Ad-hoc Networks (VANETs) have experienced significant development in recent years, playing a crucial role in enhancing the driving experience by enabling safer and more efficient inter-vehicle interactions through information exchange. Vehicle-to-Vehicle (V2V) communication is particularly vital as it not only helps to prevent collisions and improve traffic efficiency but also provides essential situational awareness to drivers or autonomous driving systems. Communication is typically supported by roadside units (RSUs); however, in practical applications, vehicles may exceed the communication range of RSUs, thus exposing them to various malicious attacks. Additionally, considering the limited computational resources of onboard units (OBUs) in vehicles, there is a high demand for designing lightweight security protocols that support V2V communication. To address this issue, this paper proposes an efficient anonymous V2V identity-authentication protocol tailored for scenarios that lack RSU support. The proposed protocol was formally assessed using the Scyther tool, demonstrating its capability to withstand major typical malicious attacks. Performance evaluations indicate that the proposed protocol is efficient in terms of communication and computational overhead, making it a viable solution for V2V communication. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) is an innovative computing paradigm with an exceptional ability to improve the vehicles' capacity to manage computation-intensive applications with both low latency and energy consumption. Moreover, more and more Artificial Intelligence (AI) technologies are applied in task offloading on the Internet of Vehicles (IoV). Focusing on the problems of computing latency and energy consumption, in this paper, we propose an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for an IoV network, which ensures reliable low-latency communication, efficient task offloading in the IoV network by using a Software-Defined Vehicular-based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in the IoV layer to implement the task allocation of each vehicle. By applying AI technologies such as reinforcement learning (RL), Markov decision process, and deep learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay. [ABSTRACT FROM AUTHOR]

In the current 5G vehicle network system, there are security issues such as wireless intrusion, privacy leakage, and remote control. To address these challenges, an improved lightweight anonymous authentication key negotiation scheme based on certificate-less aggregate signatures is proposed and its security and efficiency are analyzed. The result shows that the scheme can offer security attributes including anonymity, traceability, and revocability, as well as effective identity authentication, and it can resist forgery attacks, man-in-the-middle attacks, tampering attacks, and smart card loss attacks. Moreover, compared with similar schemes, it possesses superior security and more efficient computational efficiency and less communication overhead, thereby being more appropriate for high-speed, large-capacity, low-latency, and resource-constrained 5G vehicle network application scenarios. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet of Vehicles using centralized systems faces significant challenges, including reliability and security vulnerabilities and high latency. This paper introduces a blockchain-enabled authentication and communication network for scalable IoV to enhance security, reduce latency, and relieve the dependency on centralized infrastructures. The network applies blockchain-enabled domain name services and mutual authentication for fault tolerance consensus, such as PBFT and RAFT, featuring a primary layer of road side units and edge servers for inter-vehicle communication and a sub-layer within each vehicle for intra-vehicle communication. The study evaluates various scenarios and assesses roadside unit availability based on random distribution along vehicle routes. This paper also discusses the legal issues involved in the proposed model, highlighting that the IoV system should be governed by a contract-based decentralized IoV system comprising both smart contracts and traditional contracts. This model offers a novel approach to developing a decentralized, secure, efficient, and ethical IoV ecosystem, advancing autonomous and reliable vehicular networks. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) promises to revolutionize transportation in smart cities, but its interconnectedness raises critical security and privacy concerns. Limited computational power, diverse network technologies, and many sensors and vehicles challenge data integrity and trust in data exchange. Existing solutions, often dependent on specific environments and protocols, struggle to address these issues across the entire IoV ecosystem. This paper explores the potential of blockchain technology to address these challenges. We argue that blockchain's immutability and decentralization offer a unique solution for trust management in various IoV environments. We review existing blockchain-based algorithms and models proposed for IoV integration and propose a novel taxonomy to categorize these approaches. This taxonomy will help us analyze effective parameters, implementation methods, and evaluation metrics in the reviewed literature. According to our research, the most critical evaluation parameter for blockchain-based methods is time, including system-level service-related time parameters and solution implementation time, and 38% of existing papers simulated the approach using Hyperledger. Additionally, we will identify key challenges from integrating blockchain into the IoV landscape. By providing a comprehensive review and analysis of blockchain-based trust management solutions for IoV, this paper aims to contribute to the ongoing development of secure and reliable intelligent transportation systems. [ABSTRACT FROM AUTHOR]

With the explosion of data in the Internet of vehicles, large power consumption, low quality of service, and content delivery security issues have been brought. To solve these problems, a secure caching strategy based on content and resource allocation is proposed. Firstly, the caching optimization problem is formulated as a power consumption minimization problem with vehicle rate and resource constraints. Then, considering the characteristics of the content placement and content delivery, the power consumption optimization problem is decomposed into two sub-problems. The first sub-problem researches the impacts of vehicle preferences and content sizes on cache decisions, and a content popularity and size-aware caching strategy is proposed. The second sub-problem researches the effects of wireless resources on transmission power consumption, and an efficient content delivery strategy by iterative bandwidth and power allocation is proposed. After obtaining the optimal caching strategy, this paper combines the use of improved elliptic curve cryptography and advanced encryption standard algorithms to perform content secure placement. Simulation results demonstrate that the proposed strategy can significantly improve the power consumption and caching hit rate compared with benchmark schemes. [ABSTRACT FROM AUTHOR]

The modern Internet of Autonomous Vehicles (IoVs) has enabled the development of autonomous vehicles that can interact with each other and their surroundings, facilitating real-time data exchange and communication between vehicles, infrastructure, and the external environment. The lack of security procedures in vehicular networks and Controller Area Network (CAN) protocol leaves vehicles exposed to intrusions. One common attack type is the message injection attack, which inserts fake messages into original Electronic Control Units (ECUs) to trick them or create failures. Therefore, this paper tackles the pressing issue of cyber-attack detection in modern IoV systems, where the increasing connectivity of vehicles to the external world and each other creates a vast attack surface. The vulnerability of in-vehicle networks, particularly the CAN protocol, makes them susceptible to attacks such as message injection, which can have severe consequences. To address this, we propose an intelligent Intrusion detection system (IDS) to detect a wide range of threats utilizing machine learning techniques. However, a significant challenge lies in the inherent imbalance of car-hacking datasets, which can lead to misclassification of attack types. To overcome this, we employ various imbalanced pre-processing techniques, including NearMiss, Random over-sampling (ROS), and TomLinks, to pre-process and handle imbalanced data. Then, various Machine Learning (ML) techniques, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Naive Bayes (NB), and K-Nearest Neighbors (k-NN), are employed in detecting and predicting attack types on balanced data. We evaluate the performance and efficacy of these techniques using a comprehensive set of evaluation metrics, including accuracy, precision, F1_Score, and recall. This demonstrates how well the suggested IDS detects cyberattacks in external and intra-vehicle vehicular networks using unbalanced data on vehicle hacking. Using k-NN with various resampling techniques, the results show that the proposed system achieves 100% detection rates in testing on the Car-Hacking dataset in comparison with existing work, demonstrating the effectiveness of our approach in protecting modern vehicle systems from advanced threats. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles, people, transportation infrastructure, and networks, thereby realizing a more intelligent and efficient transportation system. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topological structure of IoV to have the high space and time complexity. Network modeling and structure recognition for 3D roads can benefit the description of topological changes for IoV. This paper proposes a 3D general road model based on discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles are analyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed and acceleration are studied. Finally, a general 3D road network model based on road section features is established. This paper also presents intersection and road section recognition methods based on the structural features of the 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted to create the simulation scenario, and the simulation results validate the general 3D road network model and the recognition method. Therefore, this work makes contributions to the field of intelligent transportation by providing a comprehensive approach to modeling the 3D road network and its topological changes in achieving efficient traffic flow and improved road safety. [ABSTRACT FROM AUTHOR]

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles (IoV) technology. The functional advantages of IoV include online communication services, accident prevention, cost reduction, and enhanced traffic regularity. Despite these benefits, IoV technology is susceptible to cyber-attacks, which can exploit vulnerabilities in the vehicle network, leading to perturbations, disturbances, non-recognition of traffic signs, accidents, and vehicle immobilization. This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning (DTL) models for Intrusion Detection Systems in the Internet of Vehicles (IDS-IoV) based on anomaly detection. IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks. These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks. Among these techniques, transfer learning models are particularly promising due to their efficacy with tagged data, reduced training time, lower memory usage, and decreased computational complexity. We evaluate DTL models against criteria including the ability to transfer knowledge, detection rate, accurate analysis of complex data, and stability. This review highlights the significant progress made in the field, showcasing how DTL models enhance the performance and reliability of IDS-IoV systems. By examining recent advancements, we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments, ensuring safer and more efficient transportation networks. [ABSTRACT FROM AUTHOR]

The literature has extensively explored vehicle ad hoc networks (VANETs) and vehicular clouds, with a common assumption in these studies being incorporating onboard units (OBUs) in vehicles. OBUs are used to collect and disseminate information between vehicles. Furthermore, numerous studies assume the presence of road infrastructure for communication. Implementing a vehicular cloud can play a vital role in aggregating data on events such as weather conditions, traffic information and accidents. This information is distributed to other vehicles, allowing drivers to make informed decisions and ensure safe driving practices. To protect privacy within the vehicular cloud, pseudonyms serve as a means of communication between vehicles and roadside units (RSU). Numerous existing approaches suggest more frequent updates to vehicle pseudonyms to reduce the likelihood of linking transmitted messages by vehicles. However, some of these strategies overlook situations where vehicle density is low, and vehicles have limited willingness to engage in the pseudonym-changing process. This article introduces an architecture that encourages vehicles to participate in the pseudonym-changing process to enhance vehicle privacy. This is achieved by issuing rewards to vehicles that can be used to access cloud services. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have emerged as efficient tools in disaster-stricken areas, facilitating efficient data dissemination for post-disaster rescue operations. However, the limited onboard energy of UAVs imposes significant constraints on their operational lifespan, thereby presenting substantial challenges for efficient data dissemination. Therefore, this work investigates a data dissemination scheme to enhance the UAVs' bandwidth efficiency in multi-UAV-enabled Internet of Vehicles, thereby reducing UAVs' energy consumption and improving overall system performance when UAVs hover along designated flight trajectories for data dissemination. Specifically, first, we present a software-defined network-based framework for data dissemination in multi-UAV-enabled IoV. According to this framework, we formulate a problem called C2BS (Coding-based Cooperative Broadcast Scheduling) that focuses on optimizing the UAVs' bandwidth efficiency by leveraging the combined benefits of coding and caching. Furthermore, we demonstrate the NP-hardness of the C2BS problem by employing a polynomial time reduction technique on the simultaneous matrix completion problem. Then, inspired by the benefits offered by genetic algorithms, we propose a novel approach called the Genetic algorithm-based Cooperative Scheduling (GCS) algorithm to address the C2BS problem. This approach encompasses a coding scheme for representing individuals, a fitness function for assessing individuals, operators (i.e., crossover and mutation) for generating offspring, a local search technique to enhance search performance, and a repair operator employed to rectify infeasible solutions. Additionally, we present an analysis of the time complexity for the GCS algorithm. Finally, we present a simulation model to evaluate the performance. Experimental findings provide evidence of the excellence of the proposed scheme. [ABSTRACT FROM AUTHOR]

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In this paper, an ultra-thin logo antenna (LGA) operating in multiple frequency bands for Internet of Vehicles (IoVs) applications was proposed. The designed antenna can cover five frequency bands, 0.86–1.01 GHz (16.0%) for LoRa communication, 1.3–1.36 GHz (4.6%) for GPS, 2.32–2.71 GHz (16.3%) for Bluetooth communication, 3.63–3.89 GHz (6.9%) for 5G communication, and 5.27–5.66 GHz (7.1%) for WLAN, as the simulation indicated. The initial antenna started with a modified coplanar waveguide (CPW)-fed circular disk monopole radiator. To create extra current paths and further excite other modes, the disk was hollowed out into the shape of the car logo of the Chinese smart EV brand XPENG composing four rhombic parasitic patches. Next, four triangular parasitic patches were inserted to improve the impedance matching of the band at 5.6 GHz. Finally, four metallic vias were loaded for adjusting resonant points and the return loss reduction. Designed on a flexible substrate, the antenna can easily bend to a certain degree in complex vehicular communication for IoV. The measured results under horizontal and vertical bending showed the LGA can operate in a bending state while maintaining good performance. The proposed LGA addresses the issue of applying one single multi-band antenna to allow vehicles to communicate over several channels, which relieves the need for a sophisticated antenna network. [ABSTRACT FROM AUTHOR]

Federated learning is an emerging distributed machine learning framework in the Internet of Vehicles (IoV). In IoV, millions of vehicles are willing to train the model to share their knowledge. Maintaining an active state means the participants must update their state to the FL server in a fixed interval and participate in the next round. However, the cost of maintaining an active state is very large when there are a huge number of participating vehicles. In this paper, we propose a distributed client selection scheme to reduce the cost of maintaining the active state for all participants. The clients with the highest evaluation are elected among the neighbors. In the evaluator, four variables are considered, including the sample quantity, available throughput, computational capability, and the quality of the local dataset. We adopt fuzzy logic as the evaluator since the closed-form solution over four variables does not exist. Extensive simulation results show that our proposal approximates the centralized client selection in terms of accuracy and can significantly reduce the communication overhead. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Vehicles (IoV), the demand for wireless spectrum resources has significantly increased. Dynamic spectrum sharing technology is regarded as a key solution to alleviate the shortage of spectrum resources. However, during the spectrum sharing process, security issues and a low utilization of the shared spectrum may arise. This study designs a consortium blockchain for trustworthy dynamic spectrum sharing in IoV environments. An improved asynchronous byzantine fault-tolerant algorithm is also designed to address the instability of signals in this scenario, and the allocation and management of spectrum resources between vehicles and base stations are further optimized using the Stackelberg game, ultimately deployed automatically through smart contracts. Simulation results show that our method not only significantly improves the system's response time but also ensures communication quality and can maintain efficient operation under high network delay and complex scenarios. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles driven by mobile edge computing, the service requests are offloaded to the roadside units (RSUs) via wireless network, reducing the service latency and enhancing the utilization of resources of RSUs. However, the high mobility of vehicles leads to the frequent switching of services, decreasing the quality of service, which fails to meet the requirements of latency-sensitive vehicular services. In this paper, we proposed a latency-aware service migration method with decision theory, named LSMD. Specifically, we first model the network architecture and introduce the transmission and computation of the service requests in detail. Then, considering the high mobility of vehicles, we analyze the dynamic change of vehicle locations and transform the service migration problem into an uncertain decision optimization problem. Afterward, we find the optimal service migration strategy with the objectives of minimizing the service latency and balancing the workload on RSUs. Finally, numerical experiment results on real-world datasets demonstrate that our method outperforms the other two baselines. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the credibility of edge nodes in the Internet of Vehicles could not be guaranteed, this paper proposed a reputation-based task offloading and resource allocation model for the Internet of Vehicles, and used the reputation of edge nodes recorded on the blockchain to evaluate its credibility, so as to help the terminal devices select reliable edge nodes for task offloading. At the same time, this paper modeled the offloading strategy as the time delay and energy consumption minimization problem under the reputation constraint, and used the multi-agent deep deterministic policy gradient algorithm to solve the approximate optimal solution of the NP-hard problem. The edge server received rewards based on the completion of task offloading, and then updated the reputation recorded on the blockchain. Simulation experiments show that the proposed algorithm reduces in terms of time delay and energy consumption by 25.58% to 27.44% compared with the benchmark testing schemes. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and Internet of Things (IoT) technologies, automotive companies are integrating federated learning into connected vehicles to provide users with smarter services. Federated learning enables vehicles to collaboratively train a global model without sharing sensitive local data, thereby mitigating privacy risks. However, the dynamic and open nature of the Internet of Vehicles (IoV) makes it vulnerable to potential attacks, where attackers may intercept or tamper with transmitted local model parameters, compromising their integrity and exposing user privacy. Although existing solutions like differential privacy and encryption can address these issues, they may reduce data usability or increase computational complexity. To tackle these challenges, we propose a conditional privacy-preserving identity-authentication scheme, CPPA-SM2, to provide privacy protection for federated learning. Unlike existing methods, CPPA-SM2 allows vehicles to participate in training anonymously, thereby achieving efficient privacy protection. Performance evaluations and experimental results demonstrate that, compared to state-of-the-art schemes, CPPA-SM2 significantly reduces the overhead of signing, verification and communication while achieving more security features. [ABSTRACT FROM AUTHOR]

Reconfigurable Intelligent Surface (RIS) has been recognized as the core technology for 6G. However, transmission signals may be blocked by obstacles due to mobility and complex transmission in vehicular scenario. In order to solve the above issue, this paper researches the RIS assisted dual function radar communication system (DFRC) in the Internet of Vehicles (IoV), and proposes an alternating optimization algorithm, named joint guaranteed radar communication (JGRC) algorithm, with sensed power and semidefinite relaxation to maximize the spectral efficiency of the communication vehicle while simultaneously ensuring the radar sensing performance of the target vehicle. Specifically, the proposed JGRC algorithm can be divided into two stages. Stage one, active beamforming of Base Station (BS) transmission is optimized with the power constraint of the sensed target vehicle. Stage two, the optimized active beamforming is applied, and the phase shift matrix is optimized. Then, RIS phase shift optimization problem is transformed into Quadratically Constrained Quadratic Programming (QCQP), and rank-1 non-convex constraint is relaxed by semidefinite relaxation, which can be transformed into a standard semidefinite programming solved by Matlab toolbox. Simulation results demonstrate that compared to the RIS element random reflection, the proposed algorithm achieves higher spectral efficiency by 9.5%. [ABSTRACT FROM AUTHOR]

In order to protect the Internet of Vehicles system from the threat of network attacks and improve the accuracy of intrusion detection, a new intrusion detection method (AQVAE-RGSNet) was proposed for the characteristics of large data flow and unbalanced attack types in the vehicle network. Firstly, the adversarial quantized variational auto encoder was used to process the vehicle network data imbalance. And it was constructed by combining the vector quantized variational auto encoder-2 and the generative adversarial network with gradient penalty to alleviate the extremely unbalanced number of samples of abnormal attack types in the dataset. Afterwards, the ResNet and improved segmented residual neural network were used to learn the input sample data and predict its attack category. The experimental results indicated that AQVAE-RGSNet achieved F1 scores of 0. 998 6 and 0. 999 7 on the vehicle networking dataset CICDS2017 and CAN-intrusion-dataset, respectively. On the premise of ensuring the best training effect, it could identify attack threats more effectively in the vehicle network. [ABSTRACT FROM AUTHOR]

Internet-of-Vehicles (IoV) plays an important part in Intelligent Transportation Systems and is widely regarded as one of the most strategic applications in smart city development. Smart spectrum resource management has received much attention from the research community as it is believed to be a promising approach for solving the spectrum resource challenge of IoV and Intelligent Transportation Systems. In this article, we propose a dynamic spectrum optimization technique based on a deep learning method for user mobility prediction. For this purpose, based on the Exploration and Preferential Return (EPR) model which can be used to investigate the movement trend and aggregation behavior of the target, we adopt the D-Exploration and Preferential Return (D-EPR) model as a deep learning technique to train a Long-Short Term Memory (LSTM) recurrent neural network (RNN) in order to predict the future locations of IoV nodes. With the predicted user's mobility, a graph theoretic algorithm is then applied to achieve spectrum reuse and optimization. Besides, our proposed deep-learning-based user mobility prediction is able to identify the user position. This paper then compares the performances of mobility prediction by traditional methods and our proposal. The outcomes of spectrum efficiency and network capacity are also provided to show the effectiveness of the proposed solution. Numerical results are provided to evaluate the prediction accuracy and network utility of our proposal with various memory histories, user distribution mechanisms and node dwell time. [ABSTRACT FROM AUTHOR]

The rapid growth of the automotive industry has exacerbated the conflict between the complex traffic environment, increasing communication demands, and limited resources. Given the imperative to mitigate traffic and network congestion, analyzing the performance of Internet of Vehicles (IoV) mesh networks is of great practical significance. Most studies focus solely on individual performance metrics and influencing factors, and the adopted simulation tools, such as OPNET, cannot achieve the dynamic link generation of IoV mesh networks. To address these problems, a network performance analysis model based on actual switches is proposed. First, a typical IoV mesh network architecture is constructed and abstracted into a mathematical model that describes how the link and topology changes over time. Then, the task generation model and the task forwarding model based on actual switches are proposed to obtain the real traffic distribution of the network. Finally, a scientific network performance indicator system is constructed. Simulation results demonstrate that, with rising task traffic and decreasing node caching capacity, the packet loss rate increases, and the task arrival rate decreases in the network. The proposed model can effectively evaluate the network performance across various traffic states and provide valuable insights for network construction and enhancement. [ABSTRACT FROM AUTHOR]

Temporal fusion approaches are critical for 3D visual perception tasks in IOV (Internet of Vehicles), but they often rely on intermediate representations without fully utilizing position information from the previous frame's detection results, which cannot compensate for the lack of depth information in visual data. In this work, we propose a novel framework called OccTr (Occupancy Transformer) that combines two temporal cues, intermediate representation and back-end representation, via occupancy map to enhance temporal fusion in object detection task. OccTr leverages attention mechanisms to perform both intermediate and back-end temporal fusion by incorporating intermediate BEV (bird's-eye view) features and back-end prediction results of the detector. Our two-stage framework includes occupancy map generation and cross-attention feature fusion. In stage one, the prediction results are converted into occupancy grid map format to generate back-end representation. In stage two, the high-resolution occupancy maps are fused with BEV features using cross-attention layers. This fused temporal cue provides a strong prior for the temporal detection process. Experimental results demonstrate the effectiveness of our method in improving detection performance, achieving an NDS (nuScenes Detection Score) metric score of 37.35% on the nuScenes test set, which is 1.94 points higher than the baseline. [ABSTRACT FROM AUTHOR]

Abstract The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.

In Internet of Vehicles (IoV), the security-threat information of various traffic elements can be exploited by hackers to attack vehicles, resulting in accidents, privacy leakage. Consequently, it is necessary to establish security-threat assessment architectures to evaluate risks of traffic elements by managing and sharing security-threat information. Unfortunately, most assessment architectures process data in a centralized manner, causing delays in query services. To address this issue, in this paper, a Hierarchical Blockchain-enabled Security threat Assessment Architecture (HBSAA) is proposed, utilizing edge chains and global chains to share data. In addition, data virtualization technology is introduced to manage multi-source heterogeneous data, and a metadata association model based on attribute graph is designed to deal with complex data relationships. In order to provide high-speed query service, the ant colony optimization of key nodes is designed, and the HBSAA prototype is also developed and the performance is tested. Experimental results on the large-scale vulnerabilities data gathered from NVD demonstrate that the HBSAA not only shields data heterogeneity, but also reduces service response time.