Vehicular Communication Networks Research Articles

PSEBVC: Provably Secure ECC and Biometric Based Authentication Framework Using Smartphone for Vehicular Cloud Environment

The Vehicular Cloud Environment (VCE) is a brand-new study field in cloud and vehicular network.It gives cars networking and sensor capabilities for V2I or V2V communication with roadside infrastructure. Cloud applications are frequently used in traffic control and road safety. A hybrid technical solution that utilises vehicle resources, cloud infrastructure, and Internet of Things (IoT) settings is needed for effective vehicular communication networking. VCE is a smart vehicular communication architecture that promotes system security, enhanced vehicle control, and self-driving cars. Due to the integration of unknown vehicles and infrastructure via the public network, security and privacy seem to be significant challenges with VCE. In this regard, we propose a PSEBVC, which is a provably secure elliptic curve cryptography (ECC) and biometric based authentication system for VCE employing smartphones. In the face of active and passive adversaries, the offered framework obtains the majority of security features and properties for secure communication. We also propose and prove a formal security model based on the random oracle concept. We also demonstrate the security analysis using the Scyther tool. In the same scenario, we evaluate the performance of our protocol against that of other frameworks. The proposed system, according to our findings, is both secure and efficient in terms of communication and processing overhead. The proposed architecture, according to our findings, provides all needed security criteria while also permitting effective communication.

Reinforcement Learning for Joint Optimization of Communication and Computation in Vehicular Networks

Ultra reliability and low latency communications (URLLC) are considered as one of the most important use cases for computation tasks in the Internet of Vehicles (IoV) edge computing networks. To meet the task requirements in IoV networks, communication and computing resources are allocated in an effective and efficient way. Thus, this paper proposes a multi-objective reinforcement learning strategy, called intelligent communication and computation resource allocation (ICCRA), which combines communication and computing resource allocation to reduce the total system cost consisting of latency and reliability. Specifically, this strategy can be decomposed into three algorithms. The algorithm called joint computation offloading and collaboration is a general framework of the strategy, it first uses the K-nearest neighbor method to select offloading layers for computation tasks, such as cloud computing layer, mobile edge computing layer, and local computing layer. Then, when selecting the local computing layer to perform the task, the algorithm called collaborative vehicle selection is used to find the target vehicle to execute collaborative computing. The allocation of communication and computing resources is defined as two independent objectives, the algorithm called multi-objective resource allocation uses the reinforcement learning to achieve the optimal solution, at the mobile edge computing layer. Simulation results show that compared with local computing, edge computing, and random computing strategies, the proposed strategy reduces the total system cost effectively. For instance, compared with edge computing strategy, the total system cost of the proposed strategy can be reduced about 50% on average with the different number of vehicle tasks.

TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4.

Effective communication in vehicular networks depends on the scheduling of wireless channel resources. There are two types of channel resource scheduling in Release 14 of the 3GPP, i.e., (1) controlled by eNodeB and (2) a distributed scheduling carried out by every vehicle, known as Autonomous Resource Selection (ARS). The most suitable resource scheduling for vehicle safety applications is the ARS mechanism. ARS includes (a) counter selection (i.e., specifying the number of subsequent transmissions) and (b) resource reselection (specifying the reuse of the same resource after counter expiry). ARS is a decentralized approach for resource selection. Therefore, resource collisions can occur during the initial selection, where multiple vehicles might select the same resource, hence resulting in packet loss. ARS is not adaptive towards vehicle density and employs a uniform random selection probability approach for counter selection and reselection. As a result, it can prevent some vehicles from transmitting in a congested vehicular network. To this end, the paper presents Truly Autonomous Resource Selection (TARS) for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles to avoid resource collisions. The paper also models the behavior of counter selection and resource block reselection on resource collisions using the Discrete Time Markov Chain (DTMC). Observation of the model is used to propose a fair policy of counter selection and resource reselection in ARS. The simulation of the proposed TARS mechanism showed better performance in terms of resource collision probability and the packet delivery ratio when compared with the LTE Mode 4 standard and with a competing approach proposed by Jianhua He et al.

New efficient certificateless scheme-based conditional privacy preservation authentication for applications in VANET

In vehicular network communications, the privacy and authentication requirements are necessary to design efficient solutions that not only ensure the authentication of the traffic-related messages, but also prevent attackers from affecting the driver's privacy, and in the meanwhile reduce the computation and communication costs. In this paper, we investigate ring signature and aggregate concepts in the certificate-less context to avoid the costs and enhance the benefits of both approaches. We propose a new framework Certificateless Aggregate scheme based on Traceable Ring Signature (CLA-TRS) that ensures conditional privacy preservation authentication in VANET communication, while significantly reducing the computational overhead for the signature verification.

Integrated Sensing and Communication-Enabled Predictive Beamforming With Deep Learning in Vehicular Networks

Accurate beam tracking in high-mobility vehicular networks has become a key issue for reliable communication link establishment. Recent work on integrated sensing and communication has shown the superiority of the sensing-assisted beam tracking. By estimating the angular parameters of vehicles based on the radar echoes, the roadside unit (RSU) can perform a so-called predictive beamforming to pair the beams with high accuracy as well as low latency. To further reduce the estimation error due to the nonlinear measurements, in this letter, we develop a deep learning-based approach to exploit the dependency between the angles and the measurements, and to obtain the angle estimates. Simulation results show that our proposed approach can improve the estimation performance and maintain reliable communications in high-mobility vehicular networks.

A Cooperative Localization Method based on V2I Communication and Distance Information in Vehicular Networks

Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment. Vehicle positions obtained using V2I communication are more accurate because the known roadside unit (RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends more on the number of RSUs; however, the high installation cost limits the use of this approach. It also depends on nonlinear localization nature. They were neglected in several research papers. In these studies, the accumulated errors increased with time due to the linearity localization problem. In the present study, a cooperative localization method based on V2I communication and distance information in vehicular networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem, but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the proposed method has superior accuracy than existing methods, giving a root mean square error of approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in fault environments.

Vehicular communication: Technology Advances and Market Analysis

Abstract: The strong and healthy wireless network of the vehicular communication is needed to enable the detailed system and semiconductor demand analysis. The report presents the automotive wireless communication such as V2V (Vehicle to Vehicle communication), V2X (Vehicle to Everything communication). It explains the formation and working of these automotive wireless protocols and the technology involved in vehicular communication like On-Board, LTE-V and VANETS. Wireless communications will give driver sixth sense what is going around them to help avoid accidents and improve traffic flow. This report also describes the DSRC (Dedicated Short-Range Communication) and also the involvement of 5G in these vehicular communication network. Besides, the road for a successful presentation of vehicular communication we likewise examined the investigation of potential security threats and the structure of a robust security engineering. The analysis carried in this report is to look at and evaluate the most important frameworks, applications, and its market demand that will recognize the future road infrastructures utilized by vehicles. Moreover, we have introduced future research issues of this technology and its scope for the future generation. The principle of this study is to investigate the running project in vehicular communication and make our road and surrounding safer from traffic and accidents. Keywords: Vehicular communication, V2V, V2X, LTE-V, VANETS, Security system, Applications, Market Demand

DDOS: data dissemination with optimized and secured path using modified particle swarm optimization in vehicular communication network (VCN)

DDOS: data dissemination with optimized and secured path using modified particle swarm optimization in vehicular communication network (VCN)

On the Outage Performance of Network NOMA (N-NOMA) Modeled by Poisson Line Cox Point Process

This paper studies the application of network non-orthogonal multiple access (N-NOMA) to vehicular communication networks, modeled by a Poisson line Cox point process (PLC). Particularly, the road infrastructure is modeled by a Poisson line point process (PLP), whereas base stations (BSs) and users are located according to 1D homogeneous Poisson point processes (HPPPs) on each road. In the considered N-NOMA scheme, there are two types of users, i.e., coordinated multi-point (CoMP) users which are far from their BSs and NOMA users which are close to their BSs, where the CoMP users and the NOMA users are served simultaneously by applying the principle of NOMA. Specifically, the BSs collaborate with each other to provide reliable transmission for the CoMP user by applying Almouti coding, while each BS employs superposition coding to opportunistically serve an additional NOMA user. Compared to a conventional OMA based scheme, where only a CoMP user is served, the proposed N-NOMA scheme significantly improves the performance in terms of outage sum rates and user connectivity, since more users are served in the same resource block. A comprehensive analytical framework is developed to characterize the system level performance of the proposed N-NOMA scheme by applying PLC. Closed form expressions for the outage probabilities achieved by the CoMP and NOMA users are obtained. Computer simulation results are provided to show the superior performance of the proposed scheme and also validate the accuracy of the developed analytical results.

Information-Centric Content Management Framework for Software Defined Internet of Vehicles Towards Application Specific Services

Software defined network (SDN) architectures are assimilated with vehicular communication networks in order to improve the real-time application support for the driving users. Internet of vehicles (IoV) paradigm provides information-centric application support for the road-side users. Information sharing through the road-side units (RSUs) influences the application services due to the frequent change in physical attributes of the vehicles. Considering the application oriented services and information handling in IoV, this article introduces information-centric content management framework (ICMF) for effective information utilization in the vehicular networks. This framework performs data acquisition, smoothing and management process for effective information analysis and better offloading. The proposed framework incorporates the functions of linear vector quantization for classifying acquired information and segregating it for maximum utilization. This quantization is recurrent in both continuous and alternating learning process to improve the reliability of information handling and management. The performance of the proposed framework is verified using simulations and the results prove its efficiency. The proposed framework is found to maximize resource utilization and offloading ratio with less analysis time and overhead. The simulation is verified for the varying density of vehicles, offloading ratio, and communication time, information utilization.

Reliable Communications for Vehicular Networks

Vehicular communications, referring to information exchange among vehicles, and infrastructures. It has attracted a lot of attentions recently due to its great potential to support intelligent transportation, various safety applications, and on-road infotainment. The aim of technologies such as Vehicle-to-Vehicl (V2V) and Vehicle to-Every-thibg (V2X) Vehicle-to very-thing is to include models of connectivity that can be used in various application contexts by vehicles. However, the routing reliability of these ever-changing networks needs to be paid special attention. The link reliability is defined as the probability that a direct communication link between two vehicles will stay continuously available over a specified period. Furthermore, the link reliability value is accurately calculated using the location, direction and velocity information of vehicles along the road.

Guest editorial: Time-critical communication and computation for intelligent vehicular networks

Vehicular networks are expected to empower auto mated driving and intelligent transportation via vehicle-to-everything (V2X) communications and edge/cloud-assisted computation, and in the meantime Cellular V2X (C-V2X) is gaining wide support from the global industrial ecosystem. The 5G NR-V2X technology is the evolution of LTE-V2X, which is expected to provide ultra-Reliable and Low-Latency Communications (uRLLC) with 1ms latency and 99.999% reliability. Nevertheless, vehicular networks still face great challenges in supporting many emerging time-critical applications, which comprise sensing, communication and computation as closed-loops.

Deep Reinforcement Learning Based Dynamic Reputation Policy in 5G Based Vehicular Communication Networks

Vehicular networks are vulnerable to various attacks from malicious vehicles or infrastructures within a network. The collaborative misbehavior detection system can be used to detect these internal or insider attacks. However, in a collaborative misbehavior detection system, an attacker may lower the detection accuracy by sending false feedback. A trust model can be used to stimulate vehicles to send true feedbacks. However, an attacker can take advantage of weak or strong reputation update methods. A dynamic trust or reputation update policy can be used to stimulate vehicles to send true feedbacks. In this paper, we propose a deep reinforcement learning based dynamic reputation update policy. In the proposed scheme, feedbacks from vehicles are combined in vehicular edge computing (VEC) servers using Dempster-Shafer theory and the results are used to predict the average number of true messages. VEC then uses deep reinforcement learning to determine the optimum reputation update policy to stimulate vehicles to send true feedbacks. In addition, through extensive simulations, we show that the proposed dynamic reputation-policy is better in terms of the average number of true feedbacks compared to the existing reputation update policy.

A Privacy-Preserving Misbehavior Detection System in Vehicular Communication Networks

5 G based vehicular communication networks support various traffic safety and infotainment use cases and rely on the periodic exchange of information. However, these messages are susceptible to several attacks which can be detected using misbehavior detection systems (MDS). MDS utilizes trust score, feedback score and other evaluation schemes to identify abnormal behavior of the vehicles. However, the trust and feedback scores used in MDS may violate the location, trajectory, or identity privacy of the vehicle. In this paper, we propose a privacy-preserving misbehavior detection system that can detect or identify misbehavior without violating the privacy of the vehicle. In the proposed method, encrypted weighted feedbacks sent from vehicles are combined using additive homomorphic properties without violating the privacy of the information. The decryption of the aggregate feedback is done securely at the trusted authority which updates the reputation score of the vehicle according to the decrypted aggregate feedback score. We have also performed comprehensive security analysis and have shown the correctness and resilience of the proposed schemes against various attacks. In addition, we have done extensive performance analysis and have shown that the computation cost of the proposed scheme is better compared to the existing schemes.

Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs.

Location privacy is a critical problem in the vehicular communication networks. Vehicles broadcast their road status information to other entities in the network through beacon messages. The beacon message content consists of the vehicle ID, speed, direction, position, and other information. An adversary could use vehicle identity and positioning information to determine vehicle driver behavior and identity at different visited location spots. A pseudonym can be used instead of the vehicle ID to help in the vehicle location privacy. These pseudonyms should be changed in appropriate way to produce uncertainty for any adversary attempting to identify a vehicle at different locations. In the existing research literature, pseudonyms are changed during silent mode between neighbors. However, the use of a short silent period and the visibility of pseudonyms of direct neighbors provides a mechanism for an adversary to determine the identity of a target vehicle at specific locations. Moreover, privacy is provided to the driver, only within the RSU range; outside it, there is no privacy protection. In this research, we address the problem of location privacy in a highway scenario, where vehicles are traveling at high speeds with diverse traffic density. We propose a Dynamic Grouping and Virtual Pseudonym-Changing (DGVP) scheme for vehicle location privacy. Dynamic groups are formed based on similar status vehicles and cooperatively change pseudonyms. In the case of low traffic density, we use a virtual pseudonym update process. We formally present the model and specify the scheme through High-Level Petri Nets (HLPN). The simulation results indicate that the proposed method improves the anonymity set size and entropy, provides lower traceability, reduces impact on vehicular network applications, and has lower computation cost compared to existing research work.

A vehicle to vehicle relay-based task offloading scheme in Vehicular Communication Networks.

Vehicular edge computing (VEC) is a potential field that distributes computational tasks between VEC servers and local vehicular terminals, hence improve vehicular services. At present, vehicles’ intelligence and capabilities are rapidly improving, which will likely support many new and exciting applications. The network resources are well-utilized by exploiting neighboring vehicles’ available resources while mitigating the VEC server’s heavy burden. However, due to the vehicles’ mobility, network topology, and the available computing resources change rapidly, which are difficult to predict. To tackle this problem, we investigate the task offloading schemes by utilizing vehicle to vehicle and vehicle to infrastructure communication modes and exploiting the vehicle’s under-utilized computation and communication resources, and taking the cost and time consumption into account. We present a promising relay task-offloading scheme in vehicular edge computing (RVEC). According to this scheme, the tasks are offloaded in a vehicle to vehicle relay for computation while being transmitted to VEC servers. Numerical results illustrate that the RVEC scheme substantially enhances the network’s overall offloading cost.

A Secure Trust-aware Cross-layer Routing Protocol for Vehicular Ad hoc Networks

VANETs currently represent one of the most prominent solutions that aim to reduce the number of road accident victims and congestion problems while improving the quality of driving. VANETs form a very dynamic open network in which vehicles exchange information and warnings about road situations and other traffic information through several routing protocols, without any intermediate control. However, the absence of a central control makes such a network vulnerable to several types of attack, not only from the outside but also, and mostly, from the interior. This makes their detection by classical security techniques more difficult and requires the development of new techniques to control the information circulating in the network. In this context, a proposed routing protocol called TDMA-aware Routing Protocol for Multi hop communication in Vehicular networks, is vulnerable to security threats, such as Black Hole and Gray Hole attacks, as well as MAC attacks such as Denial of Service (DoS), which lead to a considerable deterioration in the network’s performance in terms of packet delivery ratio, end-to-end delays, channel access rate, etc. To mitigate the effect of those attacks, we propose a trust-based model in which each node will establish a trust relationship with its neighbors based on their behaviors during the channel access and packet forwarding process. The simulation results show a significant decrease in the effect of attacks on the performance of the TRPM protocol.

Resource Allocation for Delay-Sensitive Vehicle-to-Multi-Edges (V2Es) Communications in Vehicular Networks: A Multi-Agent Deep Reinforcement Learning Approach

The rapid development of internet of vehicles (IoV) has recently led to the emergence of diverse intelligent vehicular applications such as automatic driving, auto navigation, and advanced driver assistance, etc. However, the current vehicular communication framework, such as vehicle-to-vehicle (V2V), vehicle-to-cloud (V2C), and vehicle-to-roadside infrastructure (V2I), still remain challenging in supporting these intelligent and delay-sensitive applications, due to its long communication latency or low computational capability. Besides that, the traditional vehicle network is prone to be unavailable because of the mobility with high-speed of the vehicles. To address these issues, this paper proposes a vehicle-to-multi-edges (V2Es) communication framework in vehicular networks. By utilizing the resource of edge nodes in the proximity, the emergency information or services of vehicles can be timely processed and completed, which improves the service quality of vehicles. Furthermore, we define a joint task offloading and edge caching problem, targeting optimizing both the latency of services and energy consumption of vehicles. Based on this, we propose a multi-agent reinforcement learning (RL) method to learn the dynamic communication status between vehicles and edge nodes, and make decisions on task offloading and edge caching. Finally, results of the simulation show that our proposal is able to learn the scheduling policy more quickly and effectively, and reduce the service latency by more than 10% on average.

QOS aware and secured routing algorithm using machine intelligence in next generation VANET

Vehicular ad-hoc networks have been emerging as an inevitable research topic in recent years due to its applications in Intelligent Transportation System. Establishing end-to-end communication and high authenticate information transfer in vehicular networks remains to be a real challenge due to the dynamic topology change, vehicle interspacing, high vehicle density and non-intelligent authenticated routing mechanisms. To address these aforementioned, a unique stochastic based adaptive routing integrated with secured 3D logistic chaotic maps and traffic flow predictions has been developed and referred in this paper as Stochastic Chaos based Adaptive Routing with Prediction (SCARP). The SCARP can perform the good traffic flow predictions using strong deep learning networks which recommends a stochastic based node discovery routing principle. This approach has reduced connectivity loss, minimized delay and as well provides chaotic authentication to form an efficient and safe data transmission among the vehicular nodes. The performance testing of this proposed method is done with a few months of traffic data obtained from the layout Puducherry U.T, India. These experimentations are carried out in SUMO-OMNET++ environment and compared with existing learning and routing algorithms. Simulation results demonstrates that the proposed algorithm has outperformed the other existing algorithms in terms of prediction accuracy, sensitivity, average delay, packet delivery ratio. Also the transmission performance of the proposed algorithm has more robustness with the high intensity DoS attacks.

Resource Allocation for Open-Loop Ultra-Reliable and Low-Latency Uplink Communications in Vehicular Networks

To support ultra-reliable and low-latency communication (URLLC) in vehicular networks, the virtual cells, where multiple access points (APs) cooperatively serve one mobile node, have been proposed to reduce the end-to-end latency in the downlink. The latency can be further reduced by eliminating the need for retransmission and feedback control, i.e., open-loop communications. However, it is difficult to achieve a high reliability of the uplink via virtual cells, because of multiple access interference and collisions from other virtual cells nearby. In this paper, we formulate the proactive radio resource allocation in the open-loop uplink of vehicular networks as a stochastic optimization problem with the objective to maximize the uplink reliability while ensuring the network stability. The optimal resource allocation policies are obtained solving the optimization problem using the Lyapunov optimization technique in a distributed manner. To reduce the computational and to improve the challenges of the Lyapunov optimization, we propose a virtual resource slicing algorithm that maps radio resource units to virtual resource blocks. Simulation results exhibit that both ultra-low latency and ultra high reliability are guaranteed in the open-loop uplink of vehicular networks. Based on the theoretical performance analysis and simulations, the uplink radio access procedure is summarized for the URLLC in vehicular networks.