Vehicular Network Applications Research Articles

A series‐fed frequency scanning antenna for millimeter wave wireless communication

AbstractIn the realms of 5G and the upcoming 6G communications, the millimeter‐wave bands offer substantially greater bandwidth. However, due to their propagation characteristics, aligning antennas becomes a challenging issue, particularly at the node side in industrial and vehicular network applications. Angle‐scanning antennas can significantly enhance communication reliability or provide new possibilities for spatial multiplexing. Previous studies have predominantly concentrated on frequency‐scanning antennas characterized by either low‐gain individual units or expensive digital arrays. Consequently, there has been a scarcity of solutions that are both cost‐effective and high‐gain. In this study, a method is proposed for achieving frequency scanning by extending the feed lines between elements. Based on this approach, a millimeter‐wave high‐gain serial‐feed frequency‐scanning antenna is developed. This antenna operates within the 60–64 GHz millimeter‐wave ISM (Industrial, Scientific, and Medical) band and is capable of performing a 10 scan in the E‐plane. Moreover, the antenna maintains a width of less than half a wavelength, allowing for the assembly of multiple antennas into a MIMO array.

Multiple UAVs Networking Oriented Consistent Cooperation Method Based on Adaptive Arithmetic Sine Cosine Optimization

With the rapid development of the Internet of Things, the Internet of Vehicles (IoV) has quickly drawn considerable attention from the public. The cooperative unmanned aerial vehicles (UAVs)-assisted vehicular networks, as a part of IoV, has become an emerging research spot. Due to the significant limitations of the application and service of a single UAV-assisted vehicular networks, efforts have been put into studying the use of multiple UAVs to assist effective vehicular networks. However, simply increasing the number of UAVs can lead to difficulties in information exchange and collisions caused by external interference, thereby affecting the security of the entire cooperation and networking. To address the above problems, multiple UAV cooperative formation is increasingly receiving attention. UAV cooperative formation can not only save energy loss but also achieve synchronous cooperative motion through information communication between UAVs, prevent collisions and other problems between UAVs, and improve task execution efficiency. A multi-UAVs cooperation method based on arithmetic optimization is proposed in this work. Firstly, a complete mechanical model of unmanned maneuvering was obtained by combining acceleration limitations. Secondly, based on the arithmetic sine and cosine optimization algorithm, the mathematical optimizer was used to accelerate the function transfer. Sine and cosine strategies were introduced to achieve a global search and enhance local optimization capabilities. Finally, in obtaining the precise position and direction of multi-UAVs to assist networking, the cooperation method was formed by designing the reference controller through the consistency algorithm. Experimental studies were carried out for the multi-UAVs’ cooperation with the particle model, combined with the quadratic programming problem-solving technique. The results show that the proposed quadrotor dynamic model provides basic data for cooperation position adjusting, and our simplification in the model can reduce the amount of calculations for the feedback and the parameter changes during the cooperation. Moreover, combined with a reference controller, the UAVs achieve the predetermined cooperation by offering improved navigation speed, task execution efficiency, and cooperation accuracy. Our proposed multi-UAVs cooperation method can improve the quality of service significantly on the UAV-assisted vehicular networks.

Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

A digital twin uses a virtual model of the physical system to fulfill the diverse requirements (e.g., latency, reliability, quality of physical experience) for emerging vehicular network applications. Although a twin-based implementation of vehicular networks can offer performance optimization, modeling a digital twin is a significantly challenging task. Federated learning (FL) is a better privacy-preserving, distributed learning scheme that can be used to model twin models. Although FL can offer performance enhancement, it requires careful design. Therefore, in this article, we present an overview of FL for a twinbased vehicular network. A general architecture for FL-enabled digital twins for a vehicular network is presented. Our proposed architecture consists of two spaces, such as twin space and the physical space. A physical space consists of all the physical entities (e.g., cars and edge servers) required for vehicular networks, whereas, the twin space refers to the logical space that is used for the deployment of twins. A twin space can be implemented either using edge servers and cloud servers. We also outline a few use cases of FL for the twin-based vehicular network. Finally, the paper is concluded and an outlook on open challenges is presented.

Deep reinforcement learning‐based joint task offloading and resource allocation in multipath transmission vehicular networks

AbstractMobile edge computing (MEC) is increasingly being applied to task computation with the continuing increase of computing‐intensive applications in vehicular networks. The MEC can offload the tasks generated by the vehicle to the MEC server to mitigate the computational constraints on the mobile vehicle. However, most task offloading algorithms can not cope with the dynamic distribution of vehicles in vehicular networks. Moreover, most task offloading algorithms do not consider vehicular networks using multipath transmission schemes. Therefore, it remains a challenge to implement efficient joint task offloading and resource allocation (JTORA) algorithms in multipath transmission vehicular networks. In this paper, we introduce a novel cooperative transmission network architecture in multipath transmission vehicular networks. The computational tasks generated by the vehicles can be offloaded to MEC servers deployed near the base station or to cloud servers via multipath transmission. We formulate the task offloading and resource allocation problem as a multi‐objective optimization problem in a multipath transmission vehicular network. To achieve efficient task offloading and resource allocation under cooperative transmission network architecture, we propose a JTORA algorithm for multipath transmission vehicular networks based on deep reinforcement learning (DRL). JTORA converts the optimization problem into a DRL model and obtains a specific optimization policy through model training. Simulation results show that JTORA achieves lower average task delay and lower average task cost than other traditional heuristic algorithms.

A metaheuristic MAC protocol for safety applications in cognitive vehicular networks

In a dynamic cognitive vehicular network (CVN) environment, ensuring reliable data delivery of safety messages (sm) is crucial. However, fast-moving vehicles and rapidly changing channel accessibility make sm broadcasting challenging. To address this issue, we propose a cooperative make-up technique, which leverages the 2-Hop Harris Hawks Optimization (HHO) algorithm to choose an optimal helper for sm retransmission in case of failed direct transmission. Our simulations using the Network Simulator version 2 with SUMO demonstrate that the proposed approach achieves cooperative communication within the tight 100ms sm broadcast time frame and outperforms contemporary CVN protocols regarding throughput and packet delivery ratio. Our results suggest that our approach holds great promise for reliable and efficient data delivery in CVNs.

Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Intelligent transportation system (ITS) refers to advanced applications to make transportation safer and more intelligent. The dynamic and diverse natures of the system have been creating many challenges in ITS deployment and security. The progression in recent years of machine learning provides potentially strong methods to exploit data sources from transportation networks. Machine learning-based approaches promise to deal with various challenges in networks thanks to their ability to adapt to the changing network topology and network scale. This paper investigates the application of machine-learning models in jamming detection by analyzing how the approach works on individual observations of vehicles in different scenarios. We propose a machine learning-based approach that explores hidden rules of how the observation changes under a reactive jamming attack. Our proposed machine learning-based approach improves detection accuracy compared to existing methods. The performance of the proposed method depends closely on dataset selection. Though we evaluate our approach with synthetic data, the data are generated with justifiable simulations calibrated to match a real-referenced dataset. Our analysis provides a connection between the machine-learning domain and vehicular networks by utilizing specific domain techniques for jamming detection.

A survey on efforts to apply IPv6 in V2X communication networks

This survey focuses on the application possibilities of using Internet Protocol (version 6) in Vehicle-to-Everything (V2X) networking architectures by analyzing existing standards and related papers in this field. The article explains the terminology used in IP-based V2X networks, introduces the considered use cases, and gives an overview of the three standardized options applying IPv6 in vehicular environments: IEEE WAVE provides networking services to applications in vehicular networks through IPv6, IPv6 over 802.11-OCB can be implemented in Wi-Fi-based ad hoc vehicular networks for both V2V and V2I, and the Geo Networking IPv6 adaption sub-layer (GN6ASL) for IPv6 support in the ETSI ITS protocol family. The paper also highlights non-standardized solutions and available techniques designed for IPv6-based V2X infrastructures, summarizes wireless connection requirements, and mobility management needs, together with the newest research efforts aiming at the applications of IPv6 in V2X communications.

Ambient Intelligence for Securing Intelligent Vehicular Networks: Edge-Enabled Intrusion and Anomaly Detection Strategies

The Internet of Things (IoT) is increasingly being deployed in smart city applications such as vehicular networks. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. These anomalies could range from faulty vehicular data being broadcast by the vehicles to more catastrophic attacks such as disruptive attacks and Denial of Service (DoS) attacks to name a few. This calls for a need to develop robust security schemes such as intrusion detection and anomaly detection schemes. With a humongous growth in the amount of vehicular traffic data expected, artificial intelligence (AI)-based detection strategies need to be developed to address this burgeoning demand. In this article, we propose three AI-based intrusion detection strategies for vehicular network applications, leading to an effective Ambient Intelligence based vehicular network paradigm. The detection tasks are run on local edge servers deployed at the network edge. By showing the prediction results on an experimental testbed emulating the edge servers, we show the feasibility of deploying the proposed strategies in the vehicular network scenario.

A Defense System: Jamming Detection and Mitigation for Safety Applications in Vehicular Networks

The periodically exchanged basic safety messages (BSM) in vehicular networks have become a new attack target for jamming attacks that are easy to conduct in a vehicular environment. The detection and mitigation must be cordially integrated to provide acceptable communication latency under attacking conditions. This paper considers a comprehensive defense system detecting and mitigating jamming attacks. We analyze the impact of the jamming attack on BSMs on our initially proposed random channel surfing scheme coupling with a detection method. The detection method can hardly provide 100% accuracy, and this consequently delays the reaction. We study the defense system by a mathematical model which is validated by simulations in NS-3. The obtained results depict how the performance of the channel surfing scheme depends on its preinstalled detection method

One-Hop Listening Based ARQs for Low-Latency Communication in Multihop Networks

Inspired by emerging applications in vehicular networks, we address the problem of achieving high-reliability and low-latency communication in multi-hop wireless networks. We propose a new family of Automatic-Repeat-Requests (ARQs) based cooperative strategies wherein high end-to-end reliability is obtained using packet re-transmissions at each hop while the low-latency constraint is met by imposing an upper bound on the total number of packet retransmissions across the network. A hallmark of our strategies is the one-hop listening capability wherein nodes utilize the unused ARQs of their preceding node just by counting the number of failed attempts due to decoding errors. We further extend the idea of one-hop listening to multi-hop listening, wherein a set of consecutive nodes form clusters to utilize the unused ARQs of the preceding nodes, beyond its nearest neighbour, to further improve reliability. Thus, our strategies provide the high-reliability feature with no compromise in the original latency-constraint. For the proposed strategies, we solve non-linear optimization problems on distributing the ARQs across the nodes so as to minimize packet drop probability (PDP) subject to a total number of ARQs in the network. Through extensive theoretical results on PDP and delay profiles, we show that the proposed strategies outperform the best-known strategies in this space.

A blockchain-based reputation system for trusted VANET nodes

Vehicular network applications, such as Local Danger Warnings (LDW), have become necessary as they provide services to drivers with warnings and instructions to ensure safe driving. However, before any vehicle reacts to an alert, it is essential to verify its veracity because an incorrect warning misleads other vehicles and causes unnecessary delays and detours in traffic. On the other hand, ignoring an alert or taking too long to verify it can cause accidents. A common way to address this problem is via a reputation system, where the veracity and plausibility of an alert message are assessed based on the reputation of the vehicle that discloses it. This paper describes a decentralized reputation system based on a consortium blockchain and smart contracts called BRS4VANETs. This system analyzes the reliability of data generated by a vehicle, detects malicious behavior, and contributes to decision-making. The complete functional architecture of the system, detailing the algorithms, evaluation metrics, communication protocol, and message formats, is described and implemented. Experiments with network and traffic simulators evaluated the risk of a false message attack, the effectiveness of the system and its overheads. The assessment demonstrates the importance of reputation systems and also shows the feasibility of having a decentralized system to store and disseminate reputation information based on blockchain technologies. Finally, the results also demonstrate that BRS4VANETs can successfully detect suspicious and malicious vehicles.

Energy-Efficient Cooperative Offloading for Edge Computing-Enabled Vehicular Networks

Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, inappropriate task offloading to roadside units (RSUs) can lead to large energy consumption, which will result in negative economic, environmental, and performance impacts. Therefore, in this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks. We first establish a novel cooperative offloading model to multiple RSUs for given batch of moving vehicles, different from existing works that consider single vehicle only or static users. Then, we consider the total energy minimization by optimizing the task splitting ratio, computation resource, and communication resource, which is a challenging non-convex problem, and provide optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we extend our proposed scheme to the one for a more realistic scenario (i.e., online scenario), where batches of vehicles sequentially approach the RSUs. Finally, through numerical results, the impact of network parameters on the total energy consumption is analyzed, and we verify that our proposed solution consumes lower energy than baseline schemes.

Decentralized CRL Management for Vehicular Networks With Permissioned Blockchain

In vehicular networks, authentication technology provides a basic security means to achieve trusted communication between legitimate vehicles. Revocation checking for vehicle certificates is an indispensable procedure in the process of authentication to protect vehicular networks from attacks by non-legitimate vehicles. However, revocation checking introduces procedures that requires additional time to process which challenges latency-sensitive applications in vehicular networks. This challenge grows more evidently when considering the factor of privacy preservation. In this paper, we propose to offload partial revocation task to network edges to lighten the revocation process in vehicles. Particularly, we design a method to achieve decentralized Global Certificate Revocation List (GCRL) management in network edges using permissioned blockchain technologies. In the method, both tamper-proof property and quick synchronization of the GCRL are guaranteed. Besides, our method can detect illegal revocation behaviours by validation in vehicles. Finally, we build a prototype of our proposed solution based on Hyperledger Fabric, and compare with Proof-of-Work scheme in terms of blockchain synchronization latency performance.

A method for predicting ITS cooperative applications performances

Vehicular cooperation taking place in Intelligent Transportation Systems (ITS) aims to solve traffic issues or to improve road safety for vehicles and vulnerable road users. Such cooperation is achieved through distributed applications running in the vehicular network (VANET), composed of communicating units in vehicles and infrastructure. For safety and legal reasons, it is expected that ITS services reach a satisfactory level of efficiency in the context they are deployed. However, predicting the behavior of distributed applications running in dynamic topologies remains a complex problem, which limits their deployment. In this paper, we propose a method able to predict the performances of cooperative ITS (C-ITS) applications in dynamic vehicular networks. It concerns applications relying on messages exchanges between close vehicles or between vehicles and infrastructure. The proposed method is based on an analysis of the application with respect to the vehicular traffic for determining the conditions ensuring the success of its underlying distributed algorithms and protocols. This is formalized as performances properties depending on the network dynamic. It becomes then easy to check whether these conditions are fulfilled or not in a given road traffic, either captured (and anonymized) for an existing road or expected for a new one. Thanks to an appropriate modeling, the conditions are sought and the performances of the C-ITS applications are predicted. We believe that our approach fills a gap between formal studies, VANET simulations and road tests. It will be of particular interest for the deployment of C-ITS applications. This is illustrated by several use-cases studied in realistic conditions.

Secure and ultra-reliable provenance recovery in sparse networks: Strategies and performance bounds

Provenance embedding algorithms are well known for tracking the footprints of information flow in wireless networks. Recently, low-latency provenance embedding algorithms have received traction in vehicular networks owing to strict deadlines on the delivery of packets. While existing low-latency provenance embedding methods focus on reducing the packet delay, they assume a complete graph on the underlying topology due to the mobility of the participating nodes. We identify that the complete graph assumption leads to sub-optimal performance in provenance recovery, especially when the vehicular network is sparse, which is usually observed outside peak-hour traffic conditions. As a result, we propose a two-part approach to design provenance embedding algorithms for sparse vehicular networks. In the first part, we propose secure and practical topology-learning strategies, whereas in the second part, we design provenance embedding algorithms that guarantee ultra-reliability by incorporating the topology knowledge at the destination during the provenance recovery process. Besides the novel idea of using topology knowledge for provenance recovery, a distinguishing feature for achieving ultra-reliability is the use of hash-chains in the packet, which trade communication-overhead of the packet with the complexity-overhead at the destination. We derive tight upper bounds on the performance of our strategies, and show that the derived bounds, when optimized with appropriate constraints, deliver design parameters that outperform existing methods. Finally, we also implement our ideas on OMNeT++ based simulation environment to show that their latency benefits indeed make them suitable for vehicular network applications.

Task offloading in vehicular edge computing networks via deep reinforcement learning

Given the rapid increase of various applications in vehicular networks, it is crucial to consider a flexible architecture to improve the Quality of Service (QoS). Utilizing Multi-access Edge Computing (MEC) as a distributed paradigm, with resource capabilities closer to the vehicles, would be a promising solution to reduce response time in such a network. However, MEC suffers from limited resources and is deprived of handling high mobilities with many diverse applications. This paper proposes cooperation between MEC and central cloud decisions for different vehicular application offloading. We formulate a new resource allocation problem to guarantee the required response time. To solve such an NP-hard problem, we utilize deep reinforcement learning, a proper computational model, to automatically learn the dynamics of the network state and rapidly capture an optimal solution. Extensive numerical analysis and results illustrate how our proposed scheme can achieve a high acceptance rate with a low response time.

Distributed ledger technologies in vehicular mobile edge computing: a survey

Blockchain-based systems, coined by distributed ledger technologies (DLTs), have rapidly received tremendous interest from academia, industries, and governments. Recent literature has revealed many research and developments on applying DLTs to the Internet of things (IoT), cloud-edge computing. In this survey, we conduct a comprehensive survey of the newly appeared concepts, theories, platforms, and DLTs-facilitated applications of vehicular networks and mobile edge computing (MEC). We also review the selections of the available DLTs related platforms and tools. Future research directions and issues are discussed, including security, privacy, scalability issues, and multiple applications in various domains.

A 3D Printed Nearly Isotropic Luneburg Lens Antenna for Millimeter-Wave Vehicular Networks

A three-dimensional (3D) printed Luneburg lens with nearly isotropic properties is presented. The cubical-lattice-type and the rod-type gradient-index materials are employed together to construct the novel printable spherical lens geometry, whose nearly isotropic properties improve the antenna radiation characteristics significantly. The dual-polarized radiation beam generated by the lens antenna can steer in a wide angular range with stable performance throughout a wide operating band. A prototype with an equivalent radius of 16.8 mm is printed and measured in the Ka-band. An impedance bandwidth of more than 40% for |S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> | of less than −17.5 dB, a gain of up to 21.3 dBi, and promising radiation patterns with the dual polarization scanning in an angular range of ±60° are obtained experimentally. With the advantages of satisfying operating performance and ease of realization, the lens investigated in this paper would be an attractive candidate for the wireless applications in millimeter-wave vehicular networks.

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.

A Survey of Multi-Access Edge Computing and Vehicular Networking

With the introduction of 5G and the Internet of Things, Multi-access Edge Computing (MEC) has become an evolving distributed compute and storage capability at the network edge. MEC will support task offloading, mobility, resource allocation, and management of inter-server communications to improve quality of service and satisfy real-time applications that require low latency. Thus, MEC is regarded as a crucial capability that will provide computation and storage at the network edge as an extension of the more traditional cloud. By placing MEC at the network edge, a broad range of new applications and services can be offered in close proximity to users, including support for vehicular networks. This paper provides a current and comprehensive review of MEC-enabled vehicular networks. It first introduces MEC by providing a definition, architecture, applications, and challenges. The paper then investigates MEC support for vehicular network applications and services and identifies current research and future challenges.