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Vehicle-to-Everything (V2X) Communications II
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Vehicle-to-Everything (V2X) Communications II

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (10 March 2023) | Viewed by 16966

Special Issue Editor

Dr. Omprakash Kaiwartya

E-Mail Website
Guest Editor
School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: green computing; Internet of Things; wireless sensor networks; internet of connected vehicles; heterogeneous networking; electronic vehicle charging infrastructure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, there have been a large number of advancements in communication and computation technologies, and many of these technologies are being embedded in the vehicles of the future. These vehicles, dubbed “networks-on-wheels”, are able to communicate with various elements of intelligent transportation systems, including pedestrians, vehicles, and infrastructure, and have hence led to the term vehicle-to-everything (V2X). Whether based on cellular networks or dedicated short-range communications (DSRC), V2X is the main enabler for advanced driver assistance systems (ADAS) and has the potential to make the transportation system safer, more efficient, and more environmentally friendly.

This Special Issue of the Sensors journal looks at recent research and developments in the area of V2X, as well the remaining challenges and road blocks.

Dr. Omprakash Kaiwartya
Guest Editor

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Keywords

  • intelligent vehicles
  • intelligent transportation systems
  • 5G mobile communication
  • vehicle-to-vehicle communication
  • V2X communications
  • vehicle safety
  • vehicle ad hoc networks
  • mobility management

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Related Special Issue

Published Papers (6 papers)

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Research

24 pages, 3114 KiB  
Article
Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
by Ankit Manderna, Sushil Kumar, Upasana Dohare, Mohammad Aljaidi, Omprakash Kaiwartya and Jaime Lloret
Sensors 2023, 23(21), 8772; https://doi.org/10.3390/s23218772 - 27 Oct 2023
Cited by 24 | Viewed by 2173
Abstract
Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and [...] Read more.
Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model’s performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications II)
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27 pages, 4878 KiB  
Article
5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
by Dimitrios Chatzoulis, Costas Chaikalis, Dimitrios Kosmanos, Kostas E. Anagnostou and George T. Karetsos
Sensors 2023, 23(5), 2436; https://doi.org/10.3390/s23052436 - 22 Feb 2023
Cited by 4 | Viewed by 3303
Abstract
Channel coding is a fundamental procedure in wireless telecommunication systems and has a strong impact on the data transmission quality. This effect becomes more important when the transmission must be characterised by low latency and low bit error rate, as in the case [...] Read more.
Channel coding is a fundamental procedure in wireless telecommunication systems and has a strong impact on the data transmission quality. This effect becomes more important when the transmission must be characterised by low latency and low bit error rate, as in the case of vehicle-to-everything (V2X) services. Thus, V2X services must use powerful and efficient coding schemes. In this paper, we thoroughly examine the performance of the most important channel coding schemes in V2X services. More specifically, the impact of use of 4th-Generation Long-Term Evolution (4G-LTE) turbo codes, 5th-Generation New Radio (5G-NR) polar codes and low-density parity-check codes (LDPC) in V2X communication systems is researched. For this purpose, we employ stochastic propagation models that simulate the cases of line of sight (LOS), non-line of sight (NLOS) and line of sight with vehicle blockage (NLOSv) communication. Different communication scenarios are investigated in urban and highway environments using the 3rd-Generation Partnership Project (3GPP) parameters for the stochastic models. Based on these propagation models, we investigate the performance of the communication channels in terms of bit error rate (BER) and frame error rate (FER) performance for different levels of signal to noise ratio (SNR) for all the aforementioned coding schemes and three small V2X-compatible data frames. Our analysis shows that turbo-based coding schemes have superior BER and FER performance than 5G coding schemes for the vast majority of the considered simulation scenarios. This fact, combined with the low-complexity requirements of turbo schemes for small data frames, makes them more suitable for small-frame 5G V2X services. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications II)
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21 pages, 1020 KiB  
Article
Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
by Paweł Sroka, Erik Ström, Tommy Svensson and Adrian Kliks
Sensors 2023, 23(1), 60; https://doi.org/10.3390/s23010060 - 21 Dec 2022
Cited by 2 | Viewed by 1890
Abstract
In this paper, we investigate the problem of reducing the use of radio resources for vehicle-to-vehicle communications in an autonomous platooning scenario. Achieving reliable communications, which is a key element allowing for the tight coordination of platoon vehicles’ motion, might be challenging in [...] Read more.
In this paper, we investigate the problem of reducing the use of radio resources for vehicle-to-vehicle communications in an autonomous platooning scenario. Achieving reliable communications, which is a key element allowing for the tight coordination of platoon vehicles’ motion, might be challenging in a case of heavy road traffic. Thus, in this paper, we propose to reduce the number of intra-platoon transmissions required to facilitate the safe autonomous control of vehicle mobility, by analyzing the impact of cars’ behaviors (in terms of acceleration changes) on the evolution of the inter-vehicle distance errors within the platoon. We derive formulas representing the relation between the platoon leader’s acceleration changes and the evolution of the distance error, velocity difference, and the accelerations for the first pair of vehicles. Furthermore, we propose a heuristic algorithm for selection of the intra-platoon messaging period for each platoon vehicle that minimizes the use of radio resources subject to the safety constraint, represented as the fraction of the total time when emergency braking is activated. The presented simulation results indicate that the proposed approach is capable of ensuring safe platoon operation and simultaneously providing a significant reduction in the use of resources, compared with conventional fixed-period transmission. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications II)
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13 pages, 2658 KiB  
Article
Road User Exposure from ITS-5.9 GHz Vehicular Connectivity
by Martina Benini, Marta Parazzini, Marta Bonato, Silvia Gallucci, Emma Chiaramello, Serena Fiocchi and Gabriella Tognola
Sensors 2022, 22(18), 6986; https://doi.org/10.3390/s22186986 - 15 Sep 2022
Cited by 8 | Viewed by 2463
Abstract
This study addressed an important but not yet thoroughly investigated topic regarding human exposure to radio-frequency electromagnetic fields (RF-EMF) generated by vehicular connectivity. In particular, the study assessed, by means of computational dosimetry, the RF-EMF exposure in road users near a car equipped [...] Read more.
This study addressed an important but not yet thoroughly investigated topic regarding human exposure to radio-frequency electromagnetic fields (RF-EMF) generated by vehicular connectivity. In particular, the study assessed, by means of computational dosimetry, the RF-EMF exposure in road users near a car equipped with vehicle-to-vehicle (V2V) communication antennas. The exposure scenario consisted of a 3D numerical model of a car with two V2V antennas, each fed with 1 W, operating at 5.9 GHz and an adult human model to simulate the road user near the car. The RF-EMF dose absorbed by the human model was calculated as the specific absorption rate (SAR), that is, the RF-EMF power absorbed per unit of mass. The highest SAR was observed in the skin of the head (34.7 mW/kg) and in the eyes (15 mW/kg); the SAR at the torso (including the genitals) and limbs was negligible or much lower than in the head and eyes. The SAR over the whole body was 0.19 mW/kg. The SAR was always well below the limits of human exposure in the 100 kHz–6 GHz band established by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). The proposed approach can be generalized to assess RF-EMF exposure in different conditions by varying the montage/number of V2V antennas and considering human models of different ages. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications II)
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28 pages, 5227 KiB  
Article
Feudalistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
by Tobias Renzler, Michael Stolz and Daniel Watzenig
Sensors 2022, 22(12), 4484; https://doi.org/10.3390/s22124484 - 14 Jun 2022
Cited by 3 | Viewed by 1821
Abstract
Cooperative intelligent transportation systems (C-ITSs) such as platooning rely on a robust and timely network that may not always be available in sufficient quality. Out of the box hybrid networks only partly eliminate shortcomings: mutual interference avoidance, data load balancing, and data dissemination [...] Read more.
Cooperative intelligent transportation systems (C-ITSs) such as platooning rely on a robust and timely network that may not always be available in sufficient quality. Out of the box hybrid networks only partly eliminate shortcomings: mutual interference avoidance, data load balancing, and data dissemination must be sophisticated. Lacking network quality may lead to safety bottlenecks that require that the distance between the following vehicles be increased. However, increasing gaps result in efficiency loss and additionally compromise safety as the platoon is split into smaller parts by traffic: maneuvers, e.g., cut-in maneuvers bear safety risks, and consequently lower efficiency even further. However, platoons, especially if they are very long, can negatively affect the flow of traffic. This mainly applies on entry or exit lanes, on narrow lanes, or in intersection areas: automated and non-automated vehicles in traffic do affect each other and are interdependent. To account for varying network quality and enable the coexistence of non-automated and platooned traffic, we present in this paper a new concept of platooning that unites ad hoc—in form of IEEE 802.11p—and cellular communication: feudalistic platooning. Platooned vehicles are divided into smaller groups, inseparable by surrounding traffic, and are assigned roles that determine the communication flow between vehicles, other groups and platoons, and infrastructure. Critical vehicle data are redundantly sent while the ad hoc network is only used for this purpose. The remaining data are sent—relying on cellular infrastructure once it is available—directly between vehicles with or without the use of network involvement for scheduling. The presented approach was tested in simulations using Omnet++ and Simulation of Urban Mobility (SUMO). Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications II)
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14 pages, 3339 KiB  
Article
Deep Reinforcement Learning-Based Resource Allocation for Cellular Vehicular Network Mode 3 with Underlay Approach
by Jinjuan Fu, Xizhong Qin, Yan Huang, Li Tang and Yan Liu
Sensors 2022, 22(5), 1874; https://doi.org/10.3390/s22051874 - 27 Feb 2022
Cited by 9 | Viewed by 2976
Abstract
Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links need to reuse the spectrum resources preoccupied with vehicle-to-infrastructure (V2I) links, which will interfere with the V2I [...] Read more.
Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links need to reuse the spectrum resources preoccupied with vehicle-to-infrastructure (V2I) links, which will interfere with the V2I links. Therefore, how to allocate wireless resources flexibly and improve the throughput of the V2I links while meeting the low latency requirements of the V2V links needs to be determined. This paper proposes a V2V resource allocation framework based on deep reinforcement learning. The base station (BS) uses a double deep Q network to allocate resources intelligently. In particular, to reduce the signaling overhead for the BS to acquire channel state information (CSI) in mode 3, the BS optimizes the resource allocation strategy based on partial CSI in the framework of this article. The simulation results indicate that the proposed scheme can meet the low latency requirements of V2V links while increasing the capacity of the V2I links compared with the other methods. In addition, the proposed partial CSI design has comparable performance to complete CSI. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications II)
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