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Vehicle-to-Everything (V2X) Communication Networks
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Vehicle-to-Everything (V2X) Communication Networks

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 6431

Special Issue Editors

Dr. Sabur Baidya

E-Mail Website
Guest Editor
Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: IoT; autonomous systems (UAV /CAV); edge computing; wireless networks; NFV/SDN
Dr. Yu-Jen Ku

E-Mail Website
Guest Editor
1. Department of Electrical and Computer Engineering, University of California, San Diego, CA 92161, USA
2. Staff Engineer, MediaTek USA Inc., San Jose, CA 95134, USA
Interests: edge computing; green communications; vehicular communications; wireless networks; MIMO

Special Issue Information

Dear Colleagues,

In modern smart transportation systems, vehicles and other road users are equipped with smart sensors and are interconnected to form an Internet-of-Vehicles (IoV). This facilitates advanced vehicular applications based on multi-sensor data acquisition and processing to provide efficient and safe transportation. Vehicles now can coordinate with other vehicles, bicyclists, pedestrians, road-side sensors, and infrastructures over vehicle-to-everything (V2X) communication networks. Hence, V2X communication is now bidirectional and extends to vehicle-to-infrastructure (V2I or I2V), vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P or P2V), or vehicle-to-network (V2N) communication. V2X communication enables road users to receive augmented information from other road users by incorporating various vehicular sensors and exchanging the data from those sensors over wireless technologies. 

The wireless technologies for V2X communications have evolved from Wi-Fi based vehicular ad hoc networks (VANETs), to dedicated short range communications (DSRCs), to cellular vehicle-to-everything (C-V2X) communications, and other wireless technologies. These diverse V2X applications and technologies come with various challenges in terms of their robustness, performance efficiency, safety, and security. This Special Issue focuses on the fundamental technologies, approaches, and techniques for V2X communications. Authors are encouraged to submit their research with focuses on theoretical, methodological, or practical aspects, such as simulation models, real-world experiments, algorithms, and applications concerning V2X communications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: 

  • Vehicle-to-vehicle (V2V) communications; 
  • V2I, I2V, V2P, V2N communications; 
  • Internet of Vehicles; 
  • V2X technologies, e.g., DSRC, WiFi, 5G mmWave; 
  • Cellular vehicle-to-everything (C-V2X) communications; 
  • Vehicular Edge/Fog computing; 
  • Resource allocation and self-adaptive V2X communications; 
  • Protocols, architectures, and applications for V2X communications. 

Dr. Sabur Baidya
Dr. Yu-Jen Ku
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (4 papers)

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Research

20 pages, 2407 KiB  
Article
Causality-Sensitive Scheduling to Reduce Latency in Vehicle-to-Vehicle Interactions
by Hojeong Lee, Seungmo Kang and Hyogon Kim
Sensors 2024, 24(22), 7142; https://doi.org/10.3390/s24227142 - 6 Nov 2024
Cited by 1 | Viewed by 991
Abstract
This paper shows through real-life measurement that bi-directional vehicle-to-vehicle (V2V) communication latency can be dominated by sidelink scheduling delay when causality is not taken into account. Moreover, the large delay persists for a few seconds at a time once it occurs. In applications [...] Read more.
This paper shows through real-life measurement that bi-directional vehicle-to-vehicle (V2V) communication latency can be dominated by sidelink scheduling delay when causality is not taken into account. Moreover, the large delay persists for a few seconds at a time once it occurs. In applications like maneuver coordination between autonomous vehicles or in platoon, such delay can be highly detrimental to safety and efficiency. We investigate the source of the problem and propose a solution that factors in causality in interactive communication. Specifically, we develop a constraint under which the resource positions are automatically aligned between the communicating vehicles, and the delay spikes are provably eliminated. Through the measurements on commercial V2X devices, we confirm that enforcing the constraint can remove latency spikes so that 5G sidelink can be more easily applied to time-sensitive interactions between vehicles. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication Networks)
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25 pages, 3938 KiB  
Article
Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach
by Anthony Kyung Guzmán Leguel, Hoa-Hung Nguyen, David Gómez Gutiérrez, Jinwoo Yoo and Han-You Jeong
Sensors 2024, 24(18), 6086; https://doi.org/10.3390/s24186086 - 20 Sep 2024
Cited by 1 | Viewed by 1228
Abstract
Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle’s ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept [...] Read more.
Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle’s ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL−JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication Networks)
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19 pages, 713 KiB  
Article
Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network
by Xiangyan Liu, Jianhong Zheng, Meng Zhang, Yang Li, Rui Wang and Yun He
Sensors 2024, 24(7), 2205; https://doi.org/10.3390/s24072205 - 29 Mar 2024
Cited by 4 | Viewed by 1326
Abstract
In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge [...] Read more.
In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24–29% compared with the existing state-of-the-art techniques. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication Networks)
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19 pages, 10882 KiB  
Article
Reuse Distance-Aided Resource Selection Mechanisms for NR-V2X Sidelink Communication
by Jicheng Yin and Seung-Hoon Hwang
Sensors 2024, 24(1), 253; https://doi.org/10.3390/s24010253 - 31 Dec 2023
Cited by 6 | Viewed by 2082
Abstract
Cellular vehicle-to-everything (C-V2X) facilitates direct communication between vehicles and other user equipment (UE) to improve the efficiency of the Internet of vehicles communication through sidelink. In addition, in the new radio vehicle-to-everything (NR-V2X) Mode 2, users can automatically select resources using the conventional [...] Read more.
Cellular vehicle-to-everything (C-V2X) facilitates direct communication between vehicles and other user equipment (UE) to improve the efficiency of the Internet of vehicles communication through sidelink. In addition, in the new radio vehicle-to-everything (NR-V2X) Mode 2, users can automatically select resources using the conventional sensing-based semi-persistent scheduling (SB-SPS) resource selection algorithm. This mechanism allows users to generate a list of available resources after a sensing window, after which the users can randomly select resources, and the resource can be used continuously over multiple periods before reselection. However, during the sensing window, neighbors may generate a similar list of available resources, and random selection may lead to resource conflicts. This phenomenon may lead to deteriorated communication performance and increased latency due to incorrect reception. Therefore, this paper proposes a reuse distance-aided resource selection (RD-RS) method which integrates resource reuse distance judgement with SB-SPS to mitigate resource conflicts and interference caused by random selection. Moreover, the reuse distance judgement is performed before the final resource selection, and whether the user will select the current resource depends on the reuse distance between that user and other occupiers. Furthermore, the performance of the proposed scheme is compared with other algorithms. Simulation results show that the proposed RD-RS not only achieves a higher packet reception ratio (PRR) but also effectively reduces the inter-packet gap (IPG). Moreover, in specific scenarios, the proposed method outperforms conventional schemes by 9% in terms of PRR and 70% in terms of Range. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication Networks)
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