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Vehicle-to-Everything (V2X) Communication Networks 2024–2025
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Vehicle-to-Everything (V2X) Communication Networks 2024–2025

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2928

Special Issue Editors

Dr. Qiong Wu

E-Mail Website
Guest Editor
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Interests: VAENTs; autonomous driving communication technology; edge computing and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Fan

E-Mail Website
Guest Editor
Qualcomm, San Jose, CA 95110, USA
Interests: mobile edge computing; machine learning; wireless networks; Internet of Things

Special Issue Information

Dear Colleagues,

Vehicle-to-everything (V2X) communication networks are pivotal in advancing modern transportation systems, where vehicles can communicate with each other (V2V), infrastructure (V2I), pedestrians (V2P), and networks (V2N). This technology has the potential to improve road safety and traffic efficiency. However, various challenges still exist in implementing effective V2X networks, including, but not limited to, fault-resistant communication networks for reliable information distribution, minimizing delays and data loss, and ensuring user security and privacy in diverse V2X environments.

Recent advancements in edge computing, 5G communication technologies, reinforcement learning, and federated learning provide promising solutions to these challenges. These technologies facilitate ultra-reliable and low-latency communication, intelligent network collaboration, and optimized resource allocation in V2X networks. By integrating these innovations, V2X communications not only support safer and more efficient transportation systems but also facilitate the development of autonomous driving, smart industries, vehicular networking, and space technology. The ongoing research and development of V2X promise to revolutionize the transportation landscape by addressing existing limitations and enabling new innovative applications to be realized in transportation systems. 

This Special Issue focuses on a wide range of research topics in the field of V2X communications, including, but not limited to, the following:

  • Ultra-reliable and low-latency communications (URLLC) for V2X communications.
  • New communication technologies based on 5G NR and other applications for V2X communications.
  • Modeling of routing and MAC protocol for V2X communications.
  • Federated learning-based security and privacy issues for V2X communications.
  • Machine learning-based resource management for V2X communications.
  • Reinforcement learning for V2X communications.
  • Artificial intelligence-assisted data collection and analysis for V2X communications.
  • Collaborative communication and self-organization technologies for V2X communications.
  • Sensing for V2X communications.
  • Positioning for V2X communications.
  • Cloud computing and edge computing for V2X communications.
  • Emerging applications for V2X communications.

We are pleased to invite researchers and professionals to submit original papers to this Special Issue, and look forward to your contributions. 

Dr. Qiong Wu
Dr. Qiang Fan
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.

Keywords

  • V2X communications
  • communication technologies
  • networking technologies
  • machine learning
  • positioning
  • sensing
  • cloud computing and edge computing
  • collaborative communication
  • resource allocation
  • 5G NR

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Published Papers (3 papers)

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Research

29 pages, 1776 KiB  
Article
Deep Reinforcement Learning-Enabled Computation Offloading: A Novel Framework to Energy Optimization and Security-Aware in Vehicular Edge-Cloud Computing Networks
by Waleed Almuseelem
Sensors 2025, 25(7), 2039; https://doi.org/10.3390/s25072039 - 25 Mar 2025
Viewed by 476
Abstract
The Vehicular Edge-Cloud Computing (VECC) paradigm has gained traction as a promising solution to mitigate the computational constraints through offloading resource-intensive tasks to distributed edge and cloud networks. However, conventional computation offloading mechanisms frequently induce network congestion and service delays, stemming from uneven [...] Read more.
The Vehicular Edge-Cloud Computing (VECC) paradigm has gained traction as a promising solution to mitigate the computational constraints through offloading resource-intensive tasks to distributed edge and cloud networks. However, conventional computation offloading mechanisms frequently induce network congestion and service delays, stemming from uneven workload distribution across spatial Roadside Units (RSUs). Moreover, ensuring data security and optimizing energy usage within this framework remain significant challenges. To this end, this study introduces a deep reinforcement learning-enabled computation offloading framework for multi-tier VECC networks. First, a dynamic load-balancing algorithm is developed to optimize the balance among RSUs, incorporating real-time analysis of heterogeneous network parameters, including RSU computational load, channel capacity, and proximity-based latency. Additionally, to alleviate congestion in static RSU deployments, the framework proposes deploying UAVs in high-density zones, dynamically augmenting both storage and processing resources. Moreover, an Advanced Encryption Standard (AES)-based mechanism, secured with dynamic one-time encryption key generation, is implemented to fortify data confidentiality during transmissions. Further, a context-aware edge caching strategy is implemented to preemptively store processed tasks, reducing redundant computations and associated energy overheads. Subsequently, a mixed-integer optimization model is formulated that simultaneously minimizes energy consumption and guarantees latency constraint. Given the combinatorial complexity of large-scale vehicular networks, an equivalent reinforcement learning form is given. Then a deep learning-based algorithm is designed to learn close-optimal offloading solutions under dynamic conditions. Empirical evaluations demonstrate that the proposed framework significantly outperforms existing benchmark techniques in terms of energy savings. These results underscore the framework’s efficacy in advancing sustainable, secure, and scalable intelligent transportation systems. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication Networks 2024–2025)
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20 pages, 2765 KiB  
Article
Delay/Disruption Tolerant Networking Performance Characterization in Cislunar Relay Communication Architecture
by Ding Wang, Ethan Wang and Ruhai Wang
Sensors 2025, 25(1), 195; https://doi.org/10.3390/s25010195 - 1 Jan 2025
Viewed by 895
Abstract
Future 7G/8G networks are expected to integrate both terrestrial Internet and space-based networks. Space networks, including inter-planetary Internet such as cislunar and deep-space networks, will become an integral part of future 7G/8G networks. Vehicle-to-everything (V2X) communication networks will also be a significant component [...] Read more.
Future 7G/8G networks are expected to integrate both terrestrial Internet and space-based networks. Space networks, including inter-planetary Internet such as cislunar and deep-space networks, will become an integral part of future 7G/8G networks. Vehicle-to-everything (V2X) communication networks will also be a significant component of 7G/8G networks. Therefore, space networks will eventually integrate with V2X communication networks, with both space vehicles (or spacecrafts) and terrestrial vehicles involved. DTN is the only candidate networking technology for future heterogeneous space communication networks. In this work, we study possible concatenations of different DTN convergence layer protocol adapters (CLAs) over a cislunar relay communication architecture. We present a performance characterization of the concatenations of different CLAs and the associated data transport protocols in an experimental manner. The performance of different concatenations is compared over a typical primary and secondary cislunar relay architecture. The intent is to find out which network relay path and DTN protocol configuration has the best performance over the end-to-end cislunar path. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication Networks 2024–2025)
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18 pages, 1669 KiB  
Article
Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels
by Cui Zhang, Maoxin Ji, Qiong Wu, Pingyi Fan and Qiang Fan
Sensors 2024, 24(24), 7888; https://doi.org/10.3390/s24247888 - 10 Dec 2024
Viewed by 778
Abstract
In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they [...] Read more.
In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the age of information and optimize the system’s average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles’ data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system’s AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication Networks 2024–2025)
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