V2V Communication Research Articles

Performance Evaluation for Secure Communications in Mobile Internet of Vehicles With Joint Reactive Jamming and Eavesdropping Attacks

Internet of Vehicles (IoV) is considered to be a backbone of smart transportation system where vehicles are expected to exchange traffic information and other data using vehicle-to-vehicle (V2V) communications. Furthermore, V2V communications is regarded as a medium to send upcoming traffic information to vehicles/drivers or passengers using wireless technology in a timely manner. This will help reduce traffic accidents, traffic jams, fuel consumption caused by traffic jams, loss of hours caused by delayed commuting and carbon emission caused by vehicles. Because of the critical nature of IoV for smart transportation systems, secure wireless communication is one of the critical and essential components to offer trustworthy services through IoV. Like in other wireless systems, wireless communication is open in air, which makes attackers’ job easy to launch the malicious actions in IoV. In this paper, we investigate the impact of mobility of vehicles for secure communications in IoV with joint jamming and eavesdropping attacks. We investigate different scenarios, when vehicles (legitimate ones and attackers) travel in same and opposite directions with low speed (school zone speed) limit to highway speed limit. Furthermore, we study the impact of association time and encryption time not only for establishing the connection between vehicles for V2V communications but also for exchanging messages for vehicles after successful association between vehicles and encryption of the message. In addition, we also study the joint impact of jamming and eavesdropping attacks in IoV for different speed limits, transmission ranges and wireless bandwidths. We investigate the extra cost in terms of transmit power and bandwidth incurred by jamming and eavesdropping attacks while maintaining the same secrecy rate as it could have been without those attacks. Numerical results are used to support the analysis.

A geometry-based stochastic scattering channel model for V2V communications in dense urban street environments

In this paper, we propose a stochastic channel model in three-dimensional (3D) space for multiple input multiple output (MIMO) vehicle-to-vehicle (V2V) communications in dense urban environments. The movement of the mobile transmitter and mobile receiver results in the V2V channel model behave temporal non-stationarity. Therefore, the time-varying parameters of the propagation paths and angles are derived to characterize such property. Using this channel model, we investigate the propagation characteristics of V2V channels in terms of the road section and moving time/directions/speeds of the transmitter and receiver. Numerical results show that the theoretical results of the propagation characteristics of the V2V channel model are very close to those of the simulation ones, which show that the proposed channel model is suitable for depicting the V2V communications in dense urban scenarios.

Security, Trust, and Privacy Solutions for Intelligent Internet of Vehicular Things—Part I

This articles in this special section focus on the issues and challenges related to security, trust, and privacy (STP) for the Intelligent Internet of Vehicles (IoVT). The IoT evolution have given birth to several new groundbreaking applications, such as the IoVT, smart cities, and cyber-physical systems. For IoVT, the vehicles include various smart devices to obtain and communicate information from surroundings. This information can be helpful in safe navigation, detecting hurdles, optimizing routes, and in traffic management. For efficient decision-making, in IoVs, V2V, and V2X communication have been used to compliment onboard sensor inputs and provide better services. A key concern when relying on vehicle data is vulnerable to data. Therefore, the need is to discuss the solutions that provide security, trust, and privacy (STP) to both communicating entities and secure vehicle data from malicious entities. The use of artificial intelligence (AI) in multidimensional makes them useful for the Internet of Vehicular Things. An Intelligent Internet of Vehicle Things gives the desired results in the given time constraints with less human effort. The increasing number of Internet of Vehicle Things (IoVT) results in an increase in data exploration and risk to STP.

Power Allocation and Performance Analysis in Overlay Cognitive Cooperative V2V Communication System With Outdated CSI

In this paper, an active-user cooperative scheme for overlay cognitive radio (OCR) vehicle-to-vehicle (V2V) communication system based on three-dimensional (3D) channel model is proposed. Based on the proposed cooperative scheme, the achievable rate regions of the primary users (PU) and secondary users (SU) with outdated channel state information (CSI) are analyzed. According to the tradeoff between the achievable rates of PU and SU, three power allocation schemes are proposed using outdated CSI. The performance of PU and SU in terms of outage events are analyzed. Based on the analytical framework, the simulation results for achievable rate and outage probabilities are provided. And the impact of power allocation coefficient of PU and SU, outdated CSI, signal-to-noise ratio(SNR) and the codes cross-correlation on the proposed active-user cooperation is analyzed. The simulation results are compared with validated analysis to confirm the theoretical analysis.

Robust Beam Management in Position and Velocity Aware V2V Communications Using Distributed Antenna Subarrays

Millimeter-wave vehicle-to-vehicle communication systems with multiple-input multiple-output antenna array have drawn significant attention because it is expected to satisfy the growing bandwidth requirements for the cooperative intelligent transportation systems. However, maintaining high-quality communication links between communicating vehicles without high overhead is a challenging problem due to the high mobility of vehicles. In this paper, we propose a beam management algorithm with spatially distributed antenna subarrays instead of a single co-located antenna array. Unlike existing methods, we use distributed antenna subarrays to exploit the geometric relationship of the beam directions and the relative distance between communicating vehicles. We reduce the beam alignment errors by minimizing the sum of squared errors between the estimated beam direction after the beam training process and the refined beam direction derived from the measured position and velocity data. Additionally, a position data request protocol is devised to reduce the beam refining errors. Numerical results demonstrate that the proposed beam management algorithm successfully reduces the beam alignment errors and it is robust to position and velocity measurement errors.

Safe and robust data‐driven cooperative control policy for mixed vehicle platoons

AbstractThis article considers mixed platoons consisting of both human‐driven vehicles (HVs) and automated vehicles (AVs). The uncertainties and randomness in human driving behaviors highly affect the platoon safety and stability. However, most existing control strategies are either for platoons of pure AVs, or for special formations of mixed platoons with known HV models. This article addresses the control of mixed platoons with more general formations and unknown HV models. An innovative data‐driven policy learning strategy is proposed to design the controllers for AVs based on vehicle‐to‐vehicle (V2V) communications. The policy learning strategy is embedded with the constraints of control input, inter‐vehicular distance error and V2V communication topology. The strategy establishes a safe and robustly stable mixed platoon using prescribed communication topologies. The design efficacy is verified through simulations of a mixed platoon with different communication topologies and leader velocity profiles.

CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway.

As a result of vehicle platooning, advantages including decreased traffic congestion and improved fuel economy are expected. Vehicles in a platoon move in a single line, closely spaced, and at a constant speed. Vehicle-to-vehicle communications and sensor data help keep the platoon formation in place, and the CACC system is responsible for maintaining it. In reality, V2V transmissions are essential for reducing platooning distances while still ensuring their safety and security. It is far more difficult to confirm the veracity of a V2V message’s content than it is to verify its integrity and source authentication. Only platoon members can send and receive V2V communications by implementing a practical access control mechanism. The goal is to link a prospective platoon member’s digital identification to their actual location inside the unit. A physical challenge–response interaction is used in the CAVVPM process to verify that a prospective platoon member respects the rules. The applicant is asked to perform a series of random longitudinal movements, thus, the protocol’s name. Remote attackers cannot join the platoon or send bogus CACC communications because CAVVPM blocks them. CAVVPM is more resistant to pre-recording assaults than previous work, and it can validate that the candidate is precisely behind the verifier in the same lane compared to previous studies.

Retracted: Non‐stationary 3‐D GBSM channel model for V2V communications

AbstractThe above article from IET Communications, published online on 17 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

A real-time optimal cooperative lane change strategy via V2V communication

Safety, which is in danger by careless maneuvers, is the most significant aspect of driving due to the health of passengers. The lane-changing procedure (LCP) could result in an accident provided that the driver miscalculates other vehicle positions and velocities. Consequently, automation and vehicle-to-vehicle (V2V) communication, which is a way of exchanging information between cars, have become increasingly popular owing to improving safety. Besides, real-time implementation of a fast strategy dealing with such an amount of information seems to be essential in a high-speed maneuver. This article presents a novel approach to complete the process considering optimality and safety without a huge volume of computation. This means that a constrained-optimization problem (COP) is examined to find datasets and discover simpler correlations that are effortless to implement in the real world. Ultimately, the dataset is incorporated with a model predictive controller and the effectiveness of the strategy is validated in the Siemens PreScan® platform. Moreover, the results show that the presented method is effective for a simple, safe, comfortable, and efficient lane-changing maneuver.

Using clustering scheme: Prevent reply attack in vehicular ad-hoc networks (VANET)

Many researchers work on VANET which is a hot topic for today’s research work. VANET transfers data, messages sent and makes life safe but lots of consequences also occur. Among them, there are lots of attacks that can happen in VANET. Replay attacks, DoS attacks, and DDoS attacks are among them. Many researchers work to detect replay and DDoS attacks but they do not provide any solution for preventing the attacks. Some researchers provide some researches with algorithms to prevent replay attacks. All of them apply V2I communication without clustering to prevent replay attacks but they cannot prevent it with 100% accuracy. They do not apply any clustering formula to prevent replay attacks. To prevent replay attacks I will apply V2V communication with clustering formula. By using SUMO, NS3 I will prove that applying the clustering formula prevents attacks more than without applying it to the cluster.

Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in out-of-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications \textit{before} vehicles leave the cellular network coverage. By training in simulated vehicular environments, VRLS can learn a scheduling policy that is robust and adaptable to environmental changes, thus eliminating the need for targeted (re-)training in complex real-life environments. We evaluate the performance of VRLS under varying mobility, network load, wireless channel, and resource configurations. VRLS outperforms the state-of-the-art distributed scheduling algorithm in zones without cellular network coverage by reducing the packet error rate by half in highly loaded conditions and achieving near-maximum reliability in low-load scenarios.

Sparse Code Multiple Access Assisted Resource Allocation for 5G V2X Communications

In 5G vehicle-to-everything (V2X) systems, the scarcity of spectrum resources and the inefficiency of resource allocation make vehicle-to-vehicle (V2V) communications that require stringent latency and high reliability still a challenge. Existing relevant literatures either focus on vehicle-to-infrastructure (V2I) communications, or consider scenarios where the vehicle roles (transmitter or receiver) are fixed in V2V communications, or study the resource allocation within a single cell. What’s more, considering the high-speed movement of vehicles across the coverage regions of multiple cells, the above resource allocation problem becomes even more challenging. Toward this end, we consider in this paper a multi-cell 5G V2X system where V2V links and V2I links coexist and the vehicle roles are not fixed, and propose a sparse code multiple access-based centralized resource allocation scheme, so as to address the above challenges. In view of the fact that our formulated maximizing packet reception ratio problem is a combinatorial optimization problem and is NP-hard, we design a three-stage heuristic yet joint alternating optimization approach to obtain a suboptimal solution. Extensive numerical results demonstrate the superior performances of the proposed scheme in multiple perspectives.

Pedestrian information collation through image-based person identification for pedestrian information sharing using V2V communications

Pedestrian information collation through image-based person identification for pedestrian information sharing using V2V communications

Dynamic mode selection and resource allocation approach for 5G-vehicle-to-everything (V2X) communication using asynchronous federated deep reinforcement learning method

5G vehicle-to-everything (V2X) connectivity is crucial to enable future complex vehicular networking environment for enabling intelligent transportation systems (ITS). But, for mission critical applications like safety applications, unreliable vehicle-to-vehicle (V2V) connections and heavy signaling overheads in centralized resource distribution methods are becoming key obstacles. This work discusses the popular optimization issue of the selection of transmission mode and the allocation of resources blocks for 5G-V2X communication scenario. The stated problem is conceived as a Markov decision-making mechanism, and a Decentralized Deep reinforcement Learning (DRL) algorithm is presented to optimize the aggregate potential in terms of channel capacity of vehicle-to-infrastructure users while fulfilling the latency and reliability constraints of V2V communication link sets. In addition, considering training limitation of local DRL models, a two-timed synchronous federated DRL algorithm is used for making system robust. Therefore, we use two-timescale asynchronous federated deep reinforcement learning algorithm, i.e. Large-scale model and small- scale model. In large-scale model a graph-centered vehicle clustering is done to form the cluster of neighboring vehicles on a large timescale, whereas in small timescale model the vehicles in the similar cluster are used to train using robust global asynchronous federated deep reinforcement learning algorithm. The effects of outage threshold and vehicular density with respect to the network performance is presented. The simulation findings have shown that the proposed work outperforms the other previous state of the art works. The overall preeminence and convergence of the proposed work is verified.

Impacts of Cooperative Adaptive Cruise Control Links on Driving Comfort under Vehicle-to-Vehicle Communication

Although automated vehicles could release drivers from the driving task, there are still passengers sitting in the vehicle. It is required that the driving comfort of passengers should be guaranteed. Cooperative adaptive cruise control (CACC) vehicle is of the one important type of automated vehicles using vehicle-to-vehicle (V2V) communications with various communication links. Different V2V communication links might have different driving comfort. Then, this paper focuses on exploring which link type for CACC vehicles is better from the perspective of improving driving comfort. To deal with this, car-following models of manual-driven vehicles (MDV) and CACC vehicles were first described. Then, simulations were performed using these car-following models, in which various CACC feedback link types, CACC penetration rates, and flow scenarios were taken into consideration. Simulations outputted microcosmic trajectory data of vehicles, based on which the driving comfort was evaluated using the comfort index described by the International Organization for Standardization (ISO) 2631-1. From the driving comfort perspective, simulation results suggest that CACC should monitor the immediately preceding vehicle and the third vehicle ahead when CACC penetration rates are less than approximately 50%. Additionally, if CACC penetration rates exceed 50%, the better choice is that CACC receives feedback links from two immediately successive vehicles ahead.

A novel extended Kalman filter-based optimized routing approach for IoV environment

The development of intelligent transportation system has divulged towards traffic management, planning and control that requires precise neighboring vehicle location anticipation for information transmission. The crucial problem is to maintain QoS parameters in high speed and varying vehicular topology environment. The paper presents a novel EK-PGRP (Extended Kalman filter- Predictive Geographic Routing Protocol) routing approach to anticipate neighbor location and to select the propitious neighbor for advancing packets from source to destination vehicle using extended Kalman filter for real-time V2V communication in both urban and highway vehicular environment. This is acquired according to spatial and temporal movement attributes; every vehicle has an anticipation model to anticipate its own and neighboring vehicle mobility. Moreover, if LMP (Local Maximum Problem) state is encountered, i.e., where a vehicle is unable to locate any neighbor nearer to destination than itself to forward an information packet; then it uses predictive prediction algorithm to overcome that state. The precision, robustness and coherence attributes of the proposed routing approach are illustrated via extensive simulations. EK-PGRP is contrasted with K-PGRP (Kalman filter- Predictive Geographic Routing Protocol), PGRP (Predictive Geographic Routing Protocol) and GPSR (Greedy Perimeter Stateless Routing) routing protocols and results demonstrate that EK-PGRP outperformed most of the simulation cases and attained the minimum location error while ameliorating prediction accuracy of the vehicles in vehicular environment. The simulations were performed on MATLAB R2018a along with traffic simulator SUMO.

Emergency Braking With ACC: How Much Does V2V Communication Help?

This letter provides a safety analysis for emergency braking scenarios involving consecutive vehicles. The vehicles use adaptive cruise control (ACC) with a constant-distance policy together with additional vehicle-to-vehicle (V2V) communication for emergency braking. We provide explicit formulas describing how the minimum safe inter-vehicle distance (IVD), for avoiding rear-end collision, can be shortened with the use of decentralized environmental notification messages (DENMs). More precisely, those formulas describe the dependency of such IVDs on V2V communication delay. We further show how these results can be used to compute probabilities of safe braking in the presence of packet losses.

Secure Privacy-Preserving V2V Communication in 5G-V2X Supporting Network Slicing

As autonomous driving technology continues to develop, communication for vehicles is becoming more important to provide an efficient and secure driving environment. Currently, vehicle-to-everything (V2X) based on fifth-generation (5G) is the only solution to meet the demanding requirements of self-driving cars in terms of ultra-low latency and ultra-high reliable connectivity under high mobility conditions, earning this technology more attention. Secure communication for vehicle-to-vehicle based on 5G (5G-V2V) is essential because vehicles decide how to maneuver and perceive situations based on the data exchanged. In this paper, we not only propose a new architecture for V2X based on 5G (5G-V2X) by utilizing network slicing (NS) to guarantee different features of V2X services but also analyze security requirements according to V2X service type. We also propose a secure V2V communication scheme based on 5G that satisfies these requirements. The security evaluation demonstrates that our scheme is secure against attacks on a V2V environment via both security proof and verification tool ProVerif. Through the performance evaluation, we prove that our scheme is more effective than the existing schemes while meeting end-to-end delay requirements for autonomous driving.

Resilient Cooperative Adaptive Cruise Control for Autonomous Vehicles Using Machine Learning

Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicle application that extends Adaptive Cruise Control by exploiting vehicle-to-vehicle (V2V) communication. CACC is a crucial ingredient for numerous autonomous vehicle functionalities including platooning, distributed route management, etc. Unfortunately, malicious V2V communications can subvert CACC, leading to string instability and road accidents. In this paper, we develop a novel resiliency infrastructure, RACCON, for detecting and mitigating V2V attacks on CACC. RACCON uses machine learning to develop an on-board prediction model that captures anomalous vehicular responses and performs mitigation in real time. RACCON-enabled vehicles can exploit the high efficiency of CACC without compromising safety, even under potentially adversarial scenarios. We present extensive experimental evaluation to demonstrate the efficacy of RACCON.

Dual Mode Localization Assisted Beamforming for mmWave V2V Communication

This study is motivated by the fact that localization in millimeter wave(mmWave) Vehicle-to-Vehicle communication becomes a more critical problem because both the terminals of the communication link are in motion. The positional awareness merely based on GPS or local sensors has an error margin of around 10 meters, which can worsen in uncertain real-time conditions such as road topology and highway traffic. The paper analyses the relation between vehicle dynamics, beamforming, and beam alignment for highly directive antennas. When the subsystem models presented in this paper are taken into consideration, the joint vehicle dynamics-beamforming approach will optimize the link connectivity and SNR. The vehicle dynamics model is designed to be more realistic considering the non-linear acceleration based on the throttle-brake jerks due to induced noises. The Unscented Kalman Filter based prediction subsystem predicts the alignment values even during non-linear acceleration and jerks. The feasibility of antenna design for the beamforming strategies is supported by the requirements of phase shifters and the number of elements.