Vehicular Communication Networks Research Articles

Deep-learning methods for integrated sensing and communication in vehicular networks

The integrated sensing and communication (ISAC) technique can significantly improve the overall system performance, especially in saving the bandwidth and transmitting power. In this paper, we focus on a vehicular scene of simultaneously sensing multiple targets and communicating with multiple users. We propose a signal scheme based on orthogonal frequency-division multiplexing (OFDM) and nonorthogonal multiple access (NOMA). In addition, we research the potential of applying deep-learning methods, including deep neural networks (DNNs) and convolutional neural networks (CNNs), to enhance the system's performance. A DNN-based multi-user demodulation scheme and a YOLOv5-SORT target tracking scheme in the ISAC scene are proposed, and the system realization methods are researched. The performance of the proposed system is evaluated by simulations. Under the given scene, we reach a mean-symbol-error-rate of 1×10−5 when the signal-noise-ratio is larger than 23 dB, and we reach the precision ratio, recall ratio, and the F1 value of about 0.98, 0.97, 0.97 at the confidence 0.5, respectively. In addition, the proposed schemes perform better than baselines in ISAC application scenarios.

Vehicular Networks Dynamic Grouping and Re-Orchestration Scenarios

The topological structure in vehicular communication networks presents challenges for sustaining network connectivity on the road. Highway dynamics, for example, encourage the need for an adaptive and flexible structure to handle the rapid events of vehicles joining and leaving the road. Such demand aligns with the advancement made in software-defined networks and related dynamic network re-orchestration. This paper discusses the development of a virtual model that represents the operation of an autonomous vehicular network. It also investigates the ability to re-orchestrate the topology through software definition while running the various operational phases. Network self-formation, network expansion, retraction via vehicular members joining and leaving, and network self-healing when a topological rupture occurs as a result of a key member leaving the network are the key grouping phases. The communication approach is analyzed based on the status of network members and their ability to assume the various network roles. The concept is tested using both a Contiki–Cooja network simulator and a MATLAB analytical modeling tool to reflect the operation and performance of the grouping approach under various road scenarios. The outcome of the analysis reflects the ability of the group to be formulated within a measured latency considering the various network parameters such as communication message rate. The approach offers tools for managing the dynamic connectivity of vehicular groups and may also be extended to assume the function of an on-road network digital twin during the lifetime of a given group.

Route forecasting-based authentication scheme using A* algorithm in vehicular communication network

Researchers have developed several authentication techniques for route predictions based on user requirements. These techniques estimate the shortest path and available resources in vehicular communication networks. In the current research, the existing authentication techniques for vehicular communication are compared and their inadequacies are identified. Then, new authentication technique based on route forecasting are presented for vehicular communication networks, with the service provider anticipating alternate routes for customers if the current routes have more network traffic congestion. By presenting the most efficient route, the suggested model allows users to maximise their time efficiency. Using A* algorithm, VCN agent seeks path with less network traffic congestion. This algorithm determines the shortest path between a source and a destination. Users are provided with several options by the service provider. User accepts the finest option that meets their needs. This method allows the service provider to deliver at least 15 routes within three seconds. This strategy is beneficial when a significant number of vehicles are stuck in traffic and consumers require network resources to utilise their time effectively.

Rate-Splitting Multiple Access for UAV-Based RIS-Enabled Interference-Limited Vehicular Communication System

Rate-splitting multiple access (RSMA) has emerged as a novel generalized multiple access technology which can control the interference in multi-user communication systems. Reconfigurable intelligent surface (RIS) has also been proven to improve the spectral efficiency in the next-generation (beyond 5 G (B5G) and 6 G) wireless communication networks. Thus, in this paper, we explore the RSMA scheme for RIS-enabled unmanned aerial vehicle (UAV) based multi-user vehicular communication network in the presence of co-channel interference. The communication between the UAV and the desired vehicle takes place through an RIS along with the direct link and is interfered by multiple interfering vehicles operating in the same spectrum. After deriving the approximate cumulative distribution function (CDF) of common and private stream signal-to-interference-plus-noise ratios (SINRs) at the desired vehicle in the interference limited scenario, we obtain an expression for the average outage probability (AOP) over the complete flying duration of the UAV. Furthermore, the optimum RSMA power allocation coefficients in each time slot of UAV flight duration are obtained by minimizing the Sum-AOP over all the desired vehicles. The performance of the proposed RSMA based RIS-aided vehicular communication network is compared with (a) RSMA based network without RIS and (b) non-orthogonal multiple access (NOMA) based RIS-aided counterpart. The numerical results reveal the superiority of RSMA over NOMA and the significance of deploying RIS in considered vehicular communication network. The analytical results are corroborated with Monte Carlo simulations.

Phantom Car Attack Detection via Passive Opportunistic RF Localization

A novel opportunistic approach of passive RF localization is presented for detecting “phantom car” attacks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., vehicles intentionally reporting false position/velocity information to surrounding vehicles and communication networks. Current state-of-the-art approaches for vehicle localization mostly rely on either: (i) self-reported position/velocity updates obtained via navigation technologies such as GPS, or (ii) cooperative communication approaches involving multiple vehicles exchanging situational awareness information with each other. In both cases, these approaches assume that all vehicles involved truthfully share their actual location information, which might not be the case if malicious users are present within the traffic flow. The proposed approach does not make this assumption. Rather, it extracts location information of vehicles operating on the road without the need to cooperate with them. Multiple sensors surrounding these vehicles are opportunistically and passively measured for their RF emission characteristics ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., received signal strength, time difference of arrival) based on using on-board widely available wireless signals ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., 5G, Bluetooth, WiFi). These RF emissions are not intentionally designed to be used in localization applications. Their characteristics are processed via data fusion and bounded via vehicle dynamics behavioral models before being compared against reported positions within the vehicular communications network. This paper presents the phantom car attack model used in this work to compromise conventional vehicle localization techniques, as well as the framework and its respective sub-components implementing the proposed detection approach. To evaluate the feasibility of the proposed approach, a custom-built Python-based computer simulation platform is described that accurately models the vehicular environment and its associated RF emissions characteristics. Finally, a simple hardware field experiment evaluating the performance of the proposed approach incorporating RF localization, data fusion, and vehicle behavioral dynamics modules illustrates the viability of identifying phantom car attacks within a non-cooperative framework.

Route forecasting-based authentication scheme using A* algorithm in vehicular communication network

Route forecasting-based authentication scheme using A* algorithm in vehicular communication network

Stackelberg Game-Based Deployment Design and Radio Resource Allocation in Coordinated UAVs-Assisted Vehicular Communication Networks

This paper studies optimum deployment design and radio resource allocation in a coordinated unmanned aerial vehicle (UAVs)-assisted vehicular communication network. The scarcity of radio resources and the lack of appropriate communication paths have been challenging in the existing vehicular networks. Although employing UAVs with well-designed deployments can support vehicular communications by providing line-of-sight (LoS) links, however, limited energy and spectrum scarcity issues motivate us to apply non-orthogonal multiple access (NOMA) with successive-interference-cancellation (SIC) as a solution to enhance radio resource efficiency for downlink infrastructure-to-vehicle (I2V) links. Meanwhile, due to the high complexity of the overall optimization problem that jointly handles deployment design and resource allocation for the studied UAVs-assisted vehicular network, we propose a low-complexity hierarchical suboptimal solution method. First, to efficiently find the optimum deployment of UAVs, we propose Stackelberg game with competitive nature as a game theory-based method, where UAVs are divided into leaders and followers groups that compete with each other to obtain the optimal position. Subsequently, we perform a low-complexity dynamic method in which radio resources are allocated in a sub-channel assignment algorithm and in a power allocation problem, respectively. Finally, the closed-form expressions of the optimal power allocation are derived using the KKT optimality conditions. Simulation results demonstrate improved performance for deployment design using the Stackelberg game. It is shown that NOMA improves the achievable sum rate compared to conventional orthogonal multiple access (OMA). Also, fast convergence of the proposed resource allocation and deployment design is obtained.

Data Security Enhancement in 4G Vehicular Networks Based on Reinforcement Learning for Satellite Edge Computing

The vehicular network provides the dedicated short-range communication (DSRC) with IEEE 802.11p standard. The VANET model comprises of cellular vehicle-to-everything communication with wireless communication technology. Vehicular Edge Computing exhibits the promising technology to provide promising Intelligent Transport System Services. Smart application and urban computing. Satellite edge computing model is adopted in vehicular networks to provide services to the VANET communication for the management of computational resources for the end-users to provide access to low latency services for maximal execution of service. The satellite edge computing model implemented with the 4G vehicular communication network model subjected to data security issues. This paper presented a Route Computation Deep Learning Model (RCDL) to improve security in VANET communication with 4G technology. The RCDL model uses the route establishment model with the optimal route selection. The compute route is transmitted with the cryptographic scheme model for the selection of optimal route identified from the satellite edge computing model. The proposed RCDL scheme uses the deep learning-based reinforcement learning scheme for the attack prevention in the VANET environment employed with the 4G technology communication model. The simulation results expressed that proposed RCDL model achieves the higher PDR value of 98% which is ~6% higher than the existing model. The estimation of end-to-end delay is minimal for the RCDL scheme and improves the VANET communication.

A Novel V2V-Based Temporary Warning Network for Safety Message Dissemination in Urban Environments

Vehicular communication networks (VCNs) have been widely recognized as promising solutions to support safety-related applications in urban transportation systems. However, constructing and maintaining such networks is quite challenging due to the complex traffic and communication environment. Substantial studies have focused on the design of the networking schemes and message dissemination protocols. Nonetheless, most existing designs only consider the connectivity and rapid end-to-end transmission, regardless of the network coverage and duration. In this article, we propose a novel temporary warning network (TWN) for safety message dissemination in the urban traffic environment, in which both the spatial distribution and temporal duration of the networking scheme are taken into account. Specifically, TWN is constructed by the selection of relay vehicles based on the spatiotemporal correlation of vehicle trajectory so that the safety message can be quickly disseminated within the Regions of Interest (RoIs). To maintain TWN during an accident, a reselection mechanism is also proposed, which enables newly come vehicles in the RoI to receive the messages in time. Finally, we conduct extensive numerical experiments to validate the effectiveness of our method in various traffic scenarios.

Integrated Sensing and Communications (ISAC) for Vehicular Communication Networks (VCN)

With the unprecedented development of smart vehicles and roadside units equipped with wireless connectivity, the transportation system is undergoing revolutionary changes in the past decade or two. Bearing safety and efficiency as the utmost objectives, the vehicular environments are witnessing explosive increase of various sensors onboard vehicles and equipped at transportation infrastructures. On the one hand, these sensors are destined to be wirelessly connected to provide more comprehensive situational awareness for transportation purposes. On the other hand, the abundance of sensor data of the environment can potentially shed light on the channel propagation characteristics that lie at the core of any communications system design. The integrated sensing and communications (ISACs) is henceforth both necessary and natural in vehicular communications networks (VCN). Different from existing ISAC works that target generic environments but are limited to dual-function radar-communications (DFRC), in this article we focus on transportation scenarios and applications but take a wholistic view of ISAC possibilities. First, we argue that, even though many sensors in transportation settings are nonradio-frequency (RF)-based, functional ISAC (fISAC) is feasible and necessary, in both communication-centric (CC) or sensing-centric (SC) modes. To facilitate this, the concept of synesthesia is introduced to ISAC to accommodate “machine senses” in the RF and non-RF formats. We then zoom in to RF-based sensors and propose the so-termed signaling ISAC (sISAC), with either unified-hardware (UH) or separate-hardware (SH) platforms, and delineate the unique issues arising in transportation settings. Several transportation-specific case studies are included to demonstrate these various ISAC regimes. Toward the end, the relationships of these ISAC subcategories are discussed with a roadmap laid out.

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

An Hybrid Heuristic optimal relay selection strategy for energy efficient multi hop cooperative cellular communication

One of the major constraints for mobile relay networks is energy-efficient communication owing to the restricted battery power of user terminals. Among them, the production method is cooperative cellular networks that focus on enhancing the data rate, network reliability, and network system capacity and also improving the spectrum resources in the Fifth Generation (5 G) networks. Thus, recent research works investigate the selection of mobile relays in vehicular networks and also the relay selection in cooperative communication. Here, the mobile relay is selected by the base station (operator) for every mobile user for reducing the cost with improved data rates and reducing the total transmission power. The selection of relay is performed by considering the benefits or utilities offered to three players such as mobile relay, mobile user, and operator. Conventional relay node selection approaches face challenges regarding reduced network lifetime, delay, increased energy consumption, and collusion attacks. For next-generation wireless networks, both energy efficiency and capacity are vital. To cope with these problems, a new multi-objective derived energy efficient accurate selection of relay in multi-hop cooperative cellular communication in the vehicular network through a hybrid heuristic strategy is implemented. The conventional approaches are utilized for probability-based multi-hop broadcasting for low latency. On the other hand, the major demerits of cellular communication are developing an effective multi-hop communication strategy that generates hidden node problems, and also it decreases the speed of the vehicles and the lack of infrastructure in network topology. Thus, this paper aims to propose a hybrid optimization model to choose the lowest count of multi-hops among the source and destination for the cooperative cellular vehicular communication network. The major issue well thought-out here is accurately choosing the count of hops or relays for appropriate communication, which is carried out through the multi-objective function regarding link reliability, energy consumption, outage probability, mobility factor, and transmission delay of the system. The adoption of the new hybrid algorithm with the aid of the Best Fitness-derived Crow Tunicate Swarm Optimization (BF-CTSO) algorithm is designed as the major contribution for optimally selecting the multi-hops between source and destination in a cooperative cellular communication network. Numerical results show that the proposed energy-efficient cooperative communication system outperforms traditional methods.

An Overview of the Current Challenges, Trends, and Protocols in the Field of Vehicular Communication

Intelligent transportation systems (ITS) provides a safe and reliable means of transferring data between vehicles. The document describes the transmission systems, protocols, networks, taxonomy, and applications of Intelligent Systems. Detailed analysis of the existing transmission flow systems is required, including classification, standards, coverage, applications, as well as their advantages and disadvantages. The adaptability of transmission networks, such as ad hoc, hybrid, mobile ad hoc networks (MANET), and Vehicular ad hoc networks (VANETs), has a significant advantage. Described protocols for a variety of communication types, including routing techniques, platforms, structures, and the use of information areas as well. The use of intelligent technology can determine reliable, comfortable, safe, and trustworthy vehicular communication. This paper analyzes the current vehicular communication (VC) research flow and their deployments with indicated areas where further development is necessary. This paper examines how emerging technologies in the upcoming markets will enable the development of high-featured VC technologies. The challenges of improving upon existing VC systems in the development of future systems are discussed in this paper, including medium selection, link and service quality, security, channel characteristics, and mobility. The purpose of this study is to identify the need for the development of improved VC technologies, networks, and protocols for a wide range of applications in the future.

NOMA-Enabled Backscatter Communications for Green Transportation in Automotive-Industry 5.0

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.

Age of Information Aware Resource Allocation and Packet Sampling Control in Vehicular Networks

In this letter, we analyze the age of information (AoI) outage probability for the D/Geo/1 queueing system. Then, we formulate a problem of resource allocation and packet sampling rate optimization to maximize the sum ergodic capacity of V2N links while guaranteeing the AoI outage probability of V2V links in vehicular communication networks. For each possible V2N-V2V spectrum reusing pair, the optimal power allocation and packet sampling rate are obtained by a low-complexity algorithm. Afterwards, spectrum allocation is optimized by bipartite matching. Simulation results validate our AoI analysis and show the effectiveness of the proposed algorithm.

Joint Resource Allocation and Power Control for Full-Duplex V2I Communication in High-Density Vehicular Network

It is significant to satisfy the diverse Vehicle-to-everything (V2X) Quality of Service (QoS) demands for the high-density vehicular network. The wireless full-duplex (FD) technique focuses on improving the spectrum efficiency and raising the system throughput. In this paper, the vehicle-to-vehicle (V2V) links are deployed in an FD cellular network. However, the resource allocation and power control become challenging, due to the excess self-interference (SI) at the FD base station (BS) and the interference from the V2I uplink to downlink sharing the same resource block (RB). A Dual Graph Coloring-based Interference Management (DGCIM) scheme is proposed to solve the tricky problem. The DGCIM scheme includes a GrouPing Graph Coloring with Recursive Largest First (GPGC-RLF) algorithm and a GreedY Graph Coloring (GYGC) algorithm. The simulation results illustrate the guarantee of the stringent reliability and latency constraint for V2V links. The Jain’s index of the V2I link, nearly two times that of a half-duplex (HD) scheme, further verifies the validity of the DGCIM scheme.

Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks

Utilizing the millimeter-wave (mmWave) frequency is a promising solution to meet fast-growing traffic demand over wireless networks. However, mmWave communications are sensitive to physical obstructions on signal propagation. In this paper, the reconfigurable intelligent surfaces (RISs) are investigated to overcome the limitations of mmWave communications. Particularly, an RIS is deployed to reflect the mmWave signals towards vehicular users who experience direct link blockages that may occur due to static or dynamic obstacles. To this end, a risk-averse optimization problem is designed to optimize the Base Station (BS) precoding matrix and the RIS phase shifts under stochastic link blockages. A solution approach is developed in two phases: the BS precoding optimization and the RIS phase shift control phases. In the first phase, a Decomposition and Relaxation-based Precoding Optimization (DRPO) algorithm is developed to obtain the optimal precoding matrix. In the second phase, a learning-based method is introduced to dynamically adjust the direction of reflected signals under channel uncertainty. Extensive simulations are presented to validate the efficacy of the developed algorithms. The obtained results show that the developed algorithms can ensure reliable transmissions to users in non-LoS areas and improve network performance.

A Stochastic Geometry Model for Spatially Correlated Blockage in Vehicular Networks

This article presents a stochastic geometric framework to model and analyze spatially correlated blockage in a vehicular network. First, we model the vehicular obstacles as a Boolean model on a line using stochastic geometry. Then, we represent signal paths from transmitters to receivers as a graph from the transmitter point process to the receiver point process. The blockage of a signal path occurs if and only if any obstacle obstructs the corresponding edge. Since the signal blockage occurs by obstacles, the proposed determination of blockage preserves the spatial correlation of signal paths. Under the proposed framework, we derive the blockage probability of a typical vehicle. In addition, we derive the probability that a typical vehicle is in the line of sight (LOS) with respect to at least one transmitter. The proposed framework and blockage analysis will be instrumental to the analysis of LOS-critical applications such as positioning or mmWave communications in vehicular networks.

Sensing-Oriented Communications in Vehicular Networks: Sensing Assessment and Resource Allocation

This letter studies sensing-oriented communications in vehicular networks in which multiple vehicles are equipped with visual sensors whose observations are transmitted to the cloud to estimate 3D environment model. Our aim is to maximize the sensing quality under communication constraints, which are twofold, i.e., the number of activated observations and the number of bits. We first propose information-based metrics to evaluate both accuracy and completeness of sensing in the network, and reveal the impacts of communication constraints on sensing based on eigenspace decomposition. Moreover, we design a hierarchical communication resource allocation scheme, with their performance validated by numerical results.

An Intelligent Automated System for Detecting Malicious Vehicles in Intelligent Transportation Systems.

The exponential growth of intelligent vehicles(IVs) development has resulted in a complex network. As the number of IVs in a network increases, so does the number of connections. As a result, a great deal of data is generated. This complexity leads to insecure communication, traffic congestion, security, and privacy issues in vehicular networks (VNs). In addition, detecting malicious IVs, data integration, and data validation are major issues in VNs that affect network performance. A blockchain-based model for secure communication and malicious IV detection is proposed to address the above issues. In addition, this system also addresses data integration and transaction validation using an encryption scheme for secure communication. A multi-chain concept separates the legitimate and malicious data into two chains: the Integrity chain (I-chain) and Fraud chain (F-chain). This multi-chain mechanism solves the storage problem and reduces the computing power. The integration of blockchain in the proposed model provides privacy, network security, transparency, and immutability. To address the storage issue, the InterPlanetary File System (IPFS) is integrated with Certificate Authority (CA). A reputation mechanism is introduced to detect malicious IVs in the network based on ratings. This reputation mechanism is also used to prevent Sybil attack. The evaluation of the proposed work is based on the cost of smart contracts and computation time. Furthermore, two attacker models are presented to prevent the selfish mining attack and the Sybil attack. Finally, a security analysis of the proposed smart contracts with their security vulnerabilities is also presented.