Vehicular Communication Research Articles

Dual-Wideband Structure-Reused Antenna With Diverse Radiation and Polarization Features for Vehicular Wireless Communications

Dual-Wideband Structure-Reused Antenna With Diverse Radiation and Polarization Features for Vehicular Wireless Communications

Efficient Key Extraction for Secure Vehicular Communication Through OTFS

Efficient Key Extraction for Secure Vehicular Communication Through OTFS

A Distributed Multi-Agent Deep Reinforcement Learning-Aided Transmission Design for Dynamic Vehicular Communication Networks

A Distributed Multi-Agent Deep Reinforcement Learning-Aided Transmission Design for Dynamic Vehicular Communication Networks

Toward edge-computing-enabled collision-free scheduling management for autonomous vehicles at unsignalized intersections

With the support of Vehicle-to-Everything (V2X) technology and computing power networks, the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such as de-signalization. How to effectively manage autonomous vehicles for traffic control with high throughput at unsignalized intersections while ensuring safety has been a research hotspot. This paper proposes a collision-free autonomous vehicle scheduling framework based on edge-cloud computing power networks for unsignalized intersections where the lanes entering the intersections are undirectional and designs an efficient communication system and protocol. First, by analyzing the collision point occupation time, this paper formulates an absolute value programming problem. Second, this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time (EI-OET) algorithm based on edge-cloud computing power support. Then, the communication system and protocol are designed for the proposed scheduling scheme to realize efficient and low-latency vehicular communications. Finally, simulation experiments compare the proposed scheduling framework with directional and traditional traffic light scheduling mechanisms, and the experimental results demonstrate its high efficiency, low latency, and low complexity.

B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment.

Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.

Cluster-based RSU deployment strategy for vehicular ad hoc networks with integration of communication, sensing and computing

The integration of communications, sensing and computing (I-CSC) has significant applications in vehicular ad hoc networks (VANETs). A roadside unit (RSU) plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication. Due to the constraints of limited resources, RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method. However, direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue. In this paper, we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks. The renormalization method is compared with two deployment schemes: genetic algorithm (GA) and memetic framework-based optimal RSU deployment (MFRD), to verify the improvement of communication performance. Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.

An optimized energy management and load balancing system based on cluster head selection for the vehicular network communication

An optimized energy management and load balancing system based on cluster head selection for the vehicular network communication

Effectiveness of Vehicular Communication Using NP-CSMA with Bernoulli-Based Gaussian Interpolation Function

The objective is to investigate the effectiveness of Gaussian arrival and its effect on vehicular communication compared to Bernoulli's arrival. MATLAB simulation covers three different levels of slot probability: low, medium, and high. The goal behind such simulation is to establish the importance of an adaptive function such as Gaussian interpolation resulting in smoother control of vehicular communication with better channel performance when compared to Bernoulli. This work shows that at low slot probability (Pslot), Gaussian arrival results in a much higher throughput (S) compared to Bernoulli, with a gradual reduction in throughput as Gaussian spread (γ) increases. The decrease in S as γ increases is due to the Gaussian interpolation, which performs control and results in higher channel stability. At mid probability, the simulation and analysis show a convergence between Gaussian results and Bernoulli, with differences in buffered frames (Btotal) as a function of γ. At a high Pslot value, Bernoulli produces higher S than Gaussian, with the closest Gaussian values at γ=2. However, the number of buffered frames using Bernoulli arrival is much higher than Gaussian. The exceedingly high Btotal can result in more collisions, which Gaussian arrival controls very well with a small sacrifice in throughput. The shape function for Bernoulli is shown to be different from Gaussian, except for specific values of γ, where there is a match. The obtained results show the adaptability and smoothness in which Gaussian arrival can optimize channel communication using Non-Persistent CSMA, which enables intelligent vehicular communication.

Vehicular communication using federated learning empowered chimp optimization (FLECO) algorithm

Vehicular communication using federated learning empowered chimp optimization (FLECO) algorithm

A novel wideband compact circularly‐polarized metasurface antenna for modern vehicular communication systems

AbstractIn this work, a wideband compact circularly polarized (CP) antenna using a novel polarization‐rotated metasurface (MS) structure is presented. First, the polarization‐rotated MS structure is designed by using a complementary L‐shaped MS unit cell. By optimizing the width of diagonal gap and radius of corners, the amplitude and phase of its surface impedances along ±45° can be adjusted respectively, and then a large polarization‐rotated bandwidth of 44.4% can be obtained. Secondly, the modal behaviors of the proposed MS are also investigated by adopting characteristic mode analysis (CMA) to explore feasible CP radiation. Furtherly, the proposed MS is applied to construct a wideband CP MS‐based antenna by combining with a coupling slot. The experimental results show a large 3‐dB axial ratio (AR) bandwidth (~31.1%), a large 10‐dB impedance bandwidth (~41.8%) and a compact size of 0.63λ0 × 0.63λ0 × 0.08λ0, verifying that the proposed antenna can be applied to realize a high‐quality vehicular communication.

Toward Reliable DNN-Based Task Partitioning and Offloading in Vehicular Edge Computing

Modern vehicles have become typical consumer electronics with the development of sensing, transmission, and computation technologies. The emerging intelligent transportation systems (ITSs) yield lots of deep neural network (DNN) based tasks, requiring intensive computation. In view of this, this paper makes the first exploration of accelerating the processing of DNN-based tasks while maintaining decent system reliability in vehicular edge computing (VEC) via task partitioning and offloading. Specifically, we present a specific scenario, where vehicles partition and offload their tasks to nearby vehicles and roadside infrastructures, while they may fail to receive results due to unstable vehicular communications. Then, we model the task delay by considering task properties and node capacities. On this basis, the Partitioning and Offloading Problem (POP) is formulated as a bi-objective optimization problem, to maximize both the acceleration ratio and service ratio of DNN-based tasks in VEC. Further, we propose a Distributed Partitioning and Offloading Solution (DPOS), where a delay-priority-oriented offloading strategy is designed to help edge nodes make offloading decisions, and a stacking-based partitioning strategy is designed to assist client vehicles to make partitioning decisions. Finally, we give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solution.

Planar Printed Wideband Filtering Quasi-Yagi Antenna and Its Notch-Band Design Using Parasitic Elements for Vehicular Communication

Planar Printed Wideband Filtering Quasi-Yagi Antenna and Its Notch-Band Design Using Parasitic Elements for Vehicular Communication

Transforming Transportation: Safe and Secure Vehicular Communication and Anomaly Detection With Intelligent Cyber–Physical System and Deep Learning

Transforming Transportation: Safe and Secure Vehicular Communication and Anomaly Detection With Intelligent Cyber–Physical System and Deep Learning

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

Resource allocation in vehicular network based on sparrow search algorithm and hyper-graph in the presence of multiple cellular users

PurposeSince the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).Design/methodology/approachAn optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.FindingsThe performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.Originality/valueThere is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

Design of Leaky Wave Antenna for V2V Communications

In this article, a low-profile, microstrip-based, periodic leaky wave antenna has been proposed for vehicle-to-vehicle (V2V) communications. The leaky-wave structure, loaded periodically with stepped impedance resonator stubs operates in the 5.4–6.2 GHz frequency band, which includes the WAVE (Wireless Access in Vehicular Environments) band (5.85–5.925 GHz) required for automated vehicular communications. The S11 obtained in the spectrum is below −12.68 dB. While the majority of the antennas designed for V2V communications produced omnidirectional patterns, the structure proposed in this work yields a directional pattern, preventing unnecessary outages of signals. The periodic structure gives an added advantage of beam scanning, resulting in a wider, yet directive coverage in the desired frequency band. A backward beam scan from −13˚ to −31˚ is obtained in the frequency range of 5.4–6.2 GHz with a gain variation of around ±0.6 dB only.

Clustered jamming in U-V2X communications with 3D antenna beam-width fluctuations

Jamming is the disruption of wireless communication signals, rendering them unreadable or degraded, typically caused by jamming devices. Clustered jamming in vehicular communications is a highly sophisticated attack technique characterized by the coordinated deployment of multiple jammers with the strategic intent to deliberately disrupt and incapacitate communication links, thereby presenting a formidable challenge to the reliability and security of vehicular networks. It is critical to address the intricate challenges imposed by clustered jamming in the context of vehicle-to-everything (V2X) communications augmented by unmanned aerial vehicles (UAVs), given their critical role in facilitating essential vehicle-to-vehicle and vehicle-to-UAV communication links. In this paper, we examine UAV-V2X (U-V2X) communications within the context of clustered jamming and the variations in the 3D beam-width of antennas. Also, mitigation techniques for clustered jamming in V2X communications are addressed. We consider the distribution of UAVs is governed by the Poisson point process, the distribution of vehicular-nodes (VNs) is governed by the Poisson line process, and the distribution of jammers is governed by the Matern Cluster Process. The analytical equations for the association probability and the success probability (SP) of a typical VN with the UAV and near-by VN, along with the presence of jammers and the variations of antenna beam-width are provided. Furthermore, we investigate the SP and spectral efficiency performance of the U-V2X network under different network configurations of UAVs, VNs, roads, jamming clusters, and transmit power of jamming devices. Our findings reveal that clustered jammers influence U-V2X network’s performance, further compounded by UAV beam-width fluctuations. Moreover, in low-density areas, UAVs demonstrate superior reliability for connectivity compared to VNs, despite clustered jamming. Also, lower UAV antenna fluctuations enhance UAVs’ dependability in U-V2X networks amidst clustered jamming. Specifically, for a given setup with τ= -5 dB, U-V2X link’s SP with jamming drops to one-quarter due to 2-degree beam-width fluctuations. Consequently, it is imperative to design and implement anti-jamming strategies, particularly as the number of jamming clusters and variations of antenna beam-width increase.

QoS-Driven Slicing Management for Vehicular Communications

Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries.

Advanced Road Safety: Collective Perception for Probability of Collision Estimation of Connected Vehicles

In the dynamic landscape of vehicular communication systems, connected vehicles (CVs) present unprecedented capabilities in perception, cooperation, and, notably, probability of collision management. This paper’s main concern is the collision probability of collision estimation. Achieving effective collision estimation heavily relies on the sensor perception of obstacles and a critical collision probability prediction system. This paper is dedicated to refining the estimation of collision probability through the intentional integration of CV communications, with a specific focus on the collective perception of connected vehicles. The primary objective is to enhance the understanding of the potential probability of collisions in the surrounding environment by harnessing the collective insights gathered through inter-vehicular communication and collaboration. This improvement enables a superior anticipation capacity for both the driving system and the human driver, thereby enhancing road safety. Furthermore, the incorporation of extended perception strategies holds the potential for more accurate collision probability estimation, providing the driving system or human driver with increased time to react and make informed decisions, further fortifying road safety measures. The results underscore a significant enhancement in collision probability awareness, as connected vehicles collectively contribute to a more comprehensive collision probability landscape. Consequently, this heightened collective collision probability perception improves the anticipation capacity of both the driving system and the human driver, contributing to an elevated level of road safety. For future work, the exploration of our extended perception techniques to achieve real-time probability of collision estimation is proposed. Such endeavors aim to drive the development of robust and anticipatory autonomous driving systems that truly harness the benefits of connected vehicle technologies.

A Comprehensive Study and Analysis of the Third Generation Partnership Project’s 5G New Radio for Vehicle-to-Everything Communication

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.