Recent Advances in Intelligent Vehicular Networks and Communications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 8920

Special Issue Editors


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Guest Editor
State Key Laboratory of Integrated Services Network, Xidian University, Xi’an 710071, China
Interests: in-network computing; edge computing; collaborative computing

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a platform for sharing state-of-the-art research and development on intelligent vehicular networks and communications. The content of the Special Issue will focus on the architectures and implementations, communication and networking protocols, data security and privacy, and other enabling technologies for intelligent vehicular networks and communications. Potential topics include, but are not limited to, the following:

  1. Vehicular system architecture design;
  2. Medium access control protocols in intelligent vehicular networks;
  3. Communication and network protocols in intelligent vehicular networks;
  4. Vehicle-to-vehicle/roadside/Internet communication;
  5. Artificial intelligence in intelligent vehicular networks;
  6. Mobility management and resource allocation in intelligent vehicular networks;
  7. Collaborative computing strategies for intelligent vehicular networks;
  8. Security, privacy, and trust issues of intelligent vehicular networks;
  9. Hardware and software design for intelligent vehicular networks;
  10. Implementation and testbed of intelligent vehicular networks;
  11. Blockchain-enabled vehicular networks;
  12. Intent-driven network and service management for smart vehicular communication terminals.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Dr. Zhiyuan Ren
Prof. Dr. Chen Chen
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. Electronics 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 2400 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

  • vehicular networks
  • vehicular communications
  • artificial intelligence
  • Blockchain

Published Papers (8 papers)

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Research

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26 pages, 8594 KiB  
Article
Research on the Evaluation and Prediction of V2I Channel Quality Levels in Urban Environments
by Shengli Pang, Zekang Li, Ziru Yao, Honggang Wang, Weichen Long and Ruoyu Pan
Electronics 2024, 13(5), 911; https://doi.org/10.3390/electronics13050911 - 27 Feb 2024
Viewed by 494
Abstract
The present manuscript introduces a method for evaluating and forecasting the quality of vehicle-to-infrastructure (V2I) communication channels in urban settings. This method precisely classifies and predicts channel quality levels in V2I scenarios based on long-range (LoRa) technology. This approach aims to accurately classify [...] Read more.
The present manuscript introduces a method for evaluating and forecasting the quality of vehicle-to-infrastructure (V2I) communication channels in urban settings. This method precisely classifies and predicts channel quality levels in V2I scenarios based on long-range (LoRa) technology. This approach aims to accurately classify and predict channel quality levels in V2I scenarios. The concept of channel quality scoring was first introduced, offering a more precise description of channel quality compared to traditional packet reception rate (PRR) assessments. In the channel quality assessment model based on the gated recurrent unit (GRU) algorithm, the current channel quality score of the vehicular terminal and the spatial channel parameters (SCP) of its location are utilized as inputs to achieve the classification of channel quality levels with an accuracy of 97.5%. Regarding prediction, the focus lies in forecasting the channel quality score, combined with the calculation of SCP for the vehicle’s following temporal location, thereby achieving predictions of channel quality levels from spatial and temporal perspectives. The prediction model employs the Variational Mode Decomposition-Backoff-Bidirectional Long Short-Term Memory (VMD-BO-BiLSTM) algorithm, which, while maintaining an acceptable training time, exhibits higher accuracy than other prediction algorithms, with an R2 value reaching 0.9945. This model contributes to assessing and predicting channel quality in V2I scenarios and holds significant implications for subsequent channel resource allocation. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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16 pages, 1159 KiB  
Article
Hyperbolic-Embedding-Aided Geographic Routing in Intelligent Vehicular Networks
by Ying Pan and Na Lyu
Electronics 2024, 13(3), 661; https://doi.org/10.3390/electronics13030661 - 5 Feb 2024
Viewed by 504
Abstract
Intelligent vehicular networks can not only connect various smart terminals to manned or unmanned vehicles but also to roads and people’s hands. In order to support diverse vehicle-to-everything (V2X) applications in dynamic, intelligent vehicular networks, efficient and flexible routing is fundamental but challenging. [...] Read more.
Intelligent vehicular networks can not only connect various smart terminals to manned or unmanned vehicles but also to roads and people’s hands. In order to support diverse vehicle-to-everything (V2X) applications in dynamic, intelligent vehicular networks, efficient and flexible routing is fundamental but challenging. Aimed to eliminate routing voids in traditional Euclidean geographic greedy routing strategies, we propose a hyperbolic-embedding-aided geographic routing strategy (HGR) in this paper. By embedding the network topology into a two-dimensional Poincaré hyperbolic disk, greedy forwarding is performed according to nodes’ hyperbolic coordinates. Simulation results demonstrated that the proposed HGR strategy can greatly enhance the routing success rate through a smaller stretch of the routing paths, with little sacrifice of routing computation time. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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13 pages, 13193 KiB  
Article
Multi-Frequency Channel Measurement and Characteristic Analysis in Forested Scenario for Emergency Rescue
by Wei Guo, Rong Yuan, Hui Ma, Yongxia Yuan, Dan Fei, Ke Guan, Haoran Chen, Yufei Shen, Yudong Fang and Wenchi Cheng
Electronics 2024, 13(2), 396; https://doi.org/10.3390/electronics13020396 - 18 Jan 2024
Viewed by 665
Abstract
Wireless communication has been widely used in emergency rescue, including from command vehicles to command vehicles, command vehicles to rescue teams, command vehicles to wireless sensors, and rescue teams to intelligent platforms such as unmanned vehicles. Compared to those used in cities and [...] Read more.
Wireless communication has been widely used in emergency rescue, including from command vehicles to command vehicles, command vehicles to rescue teams, command vehicles to wireless sensors, and rescue teams to intelligent platforms such as unmanned vehicles. Compared to those used in cities and suburbs, when the same communication equipment is used in forests, its communication performance, such as transmission distance, is entirely different. The main reason for this phenomenon is the extraordinary complexity of wireless signal propagation in forest scenarios. Therefore, in order to accurately and quantitatively describe the wireless channel characteristics in forest scenarios, a frequency channel measurement activity is conducted in a forest and analysis is performed to acquire the channel characteristics in forest scenarios. The measurements are carried out at 380 MHz, 640 MHz, and 1420 MHz in virgin forest. Based on the measurement data, the average power delay profile (APDP) is obtained, and multipath components (MPCs) are extracted. Root mean square (RMS) delay spread and path loss (PL) are analyzed according to MPCs. Furthermore, a new path loss model is proposed. Finally, a new path loss model and relative analysis are provided. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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19 pages, 12688 KiB  
Article
A Machine Learning-Based Interest Flooding Attack Detection System in Vehicular Named Data Networking
by Arif Hussain Magsi, Syed Agha Hassnain Mohsan, Ghulam Muhammad and Suhni Abbasi
Electronics 2023, 12(18), 3870; https://doi.org/10.3390/electronics12183870 - 13 Sep 2023
Cited by 3 | Viewed by 1160
Abstract
A vehicular ad hoc network (VANET) has significantly improved transportation efficiency with efficient traffic management, driving safety, and delivering emergency messages. However, existing IP-based VANETs encounter numerous challenges, like security, mobility, caching, and routing. To cope with these limitations, named data networking (NDN) [...] Read more.
A vehicular ad hoc network (VANET) has significantly improved transportation efficiency with efficient traffic management, driving safety, and delivering emergency messages. However, existing IP-based VANETs encounter numerous challenges, like security, mobility, caching, and routing. To cope with these limitations, named data networking (NDN) has gained significant attention as an alternative solution to TCP/IP in VANET. NDN offers promising features, like intermittent connectivity support, named-based routing, and in-network content caching. Nevertheless, NDN in VANET is vulnerable to a variety of attacks. On top of attacks, an interest flooding attack (IFA) is one of the most critical attacks. The IFA targets intermediate nodes with a storm of unsatisfying interest requests and saturates network resources such as the Pending Interest Table (PIT). Unlike traditional rule-based statistical approaches, this study detects and prevents attacker vehicles by exploiting a machine learning (ML) binary classification system at roadside units (RSUs). In this connection, we employed and compared the accuracy of five (5) ML classifiers: logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB) on a publicly available dataset implemented on the ndnSIM simulator. The experimental results demonstrate that the RF classifier achieved the highest accuracy (94%) in detecting IFA vehicles. On the other hand, we evaluated an attack prevention system on Python that enables intermediate vehicles to accept or reject interest requests based on the legitimacy of vehicles. Thus, our proposed IFA detection technique contributes to detecting and preventing attacker vehicles from compromising the network resources. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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15 pages, 1137 KiB  
Article
Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
by Ben St. Amour and Arunita Jaekel
Electronics 2023, 12(18), 3790; https://doi.org/10.3390/electronics12183790 - 7 Sep 2023
Cited by 1 | Viewed by 1071
Abstract
Vehicular ad hoc networks (VANETs) facilitate communication among vehicles and possess designated infrastructure nodes to improve road safety and traffic flow. As the number of vehicles increases, the limited bandwidth of the wireless channel used for vehicle-to-vehicle (V2V) communication can become congested, leading [...] Read more.
Vehicular ad hoc networks (VANETs) facilitate communication among vehicles and possess designated infrastructure nodes to improve road safety and traffic flow. As the number of vehicles increases, the limited bandwidth of the wireless channel used for vehicle-to-vehicle (V2V) communication can become congested, leading to packets being dropped or delayed. VANET congestion control techniques attempt to address this by adjusting different transmission parameters, including the data rate, message rate, and transmission power. In this paper, we propose a decentralized congestion control algorithm where each factor adjusts the data rate (bitrate) used to transmit its wireless packet congestion based on the current load on the channel. The channel load is estimated independently by each vehicle using the measured channel busy ratio (CBR). The simulation results demonstrate that the proposed approach outperforms existing data rate-based algorithms, in terms of both packet reception and overall channel load. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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16 pages, 10555 KiB  
Article
Co-Simulation Platform with Hardware-in-the-Loop Using RTDS and EXata for Smart Grid
by Peng Gong, Haowei Yang, Haiqiao Wu, Huibo Li, Yu Liu, Zhenheng Qi, Weidong Wang, Dapeng Wu and Xiang Gao
Electronics 2023, 12(17), 3710; https://doi.org/10.3390/electronics12173710 - 2 Sep 2023
Viewed by 1159
Abstract
The modern smart grid is a vital component of national development and is a complex coupled network composed of power and communication networks. The faults or attacks of either network may cause the performance of a power grid to decline or result in [...] Read more.
The modern smart grid is a vital component of national development and is a complex coupled network composed of power and communication networks. The faults or attacks of either network may cause the performance of a power grid to decline or result in a large-scale power outage, leading to significant economic losses. To assess the impact of grid faults or attacks, hardware-in-the-loop (HIL) simulation tools that integrate real grid networks and software virtual networks (SVNs) are used. However, scheduling faults and modifying model parameters using most existing simulators can be challenging, and traditional HIL interfaces only support a single device. To address these limitations, we designed and implemented a grid co-simulation platform that could dynamically simulate grid faults and evaluate grid sub-nets. This platform used RTDS and EXata as power and communication simulators, respectively, integrated using a protocol conversion module to synchronize and convert protocol formats. Additionally, the platform had a programmable fault configuration interface (PFCI) to modify model parameters and a real sub-net access interface (RSAI) to access physical grid devices or sub-nets in the SVN, improving simulation accuracy. We also conducted several tests to demonstrate the effectiveness of the proposed platform. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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20 pages, 10175 KiB  
Article
A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning
by Yanan Zhang, Zhen Guo and Tao Sun
Electronics 2023, 12(13), 2873; https://doi.org/10.3390/electronics12132873 - 29 Jun 2023
Viewed by 1096
Abstract
In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, [...] Read more.
In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, during non-intrusive vehicle central control screen image testing, there is a deficiency of suitable datasets and detection methods. This deficiency renders information within vehicle application images difficult to be accurately extracted by the detection network. To address this problem, this study first constructs a dataset tailored for text detection and recognition on vehicle screens. This dataset encompasses a variety of vehicle central control images, enabling the generic text detection and recognition network to more effectively identify and interpret text within vehicle screens. Subsequently, this research proposes an enhanced Fully Convolutional Networks for Text Detection (FOTS) method for vehicle central control screen text detection and recognition. This method elevates the semantic expression capabilities of features by sharing vehicle central control screen text detection and recognition features. Furthermore, it improves multi-scale feature processing capabilities through the utilization of a feature transformation module. Validation through visual and quantitative experiments demonstrates that the proposed method can effectively accomplish text detection and recognition tasks on vehicle screens. This achievement bears significant implications for the field of automated testing in IoV applications. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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Review

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29 pages, 3770 KiB  
Review
Packet Reordering in the Era of 6G: Techniques, Challenges, and Applications
by Jiaqi Lin, Xiaofeng Zhang, Xianming Gao, Pengtao Kang, Yuxi Zhou, Ying Ouyang and Tao Feng
Electronics 2023, 12(14), 3023; https://doi.org/10.3390/electronics12143023 - 10 Jul 2023
Viewed by 1746
Abstract
The advent of sixth-generation (6G) networks brings unmatched speed, reliability, and capacity for massive connections, making it a cornerstone for revolutionary applications. One such application is in vehicular networks, which have their unique demands and complexities. Specifically, they face the complex issue of [...] Read more.
The advent of sixth-generation (6G) networks brings unmatched speed, reliability, and capacity for massive connections, making it a cornerstone for revolutionary applications. One such application is in vehicular networks, which have their unique demands and complexities. Specifically, they face the complex issue of packet reordering due to the high-speed movement of vehicles and frequent switching of network connections. This paper examines the impact and causes of packet reordering, its threats to network efficiency, and potential countermeasures, particularly in the context of 6G-enabled vehicular networks. We introduce end-to-end methods and metrics to address packet reordering in 6G, discussing the development trends and application prospects. Our findings highlight the emergence of sophisticated strategies, such as prediction and avoidance, to manage packet reordering. They also reveal potential applications to boost network reliability, emulate traffic distributions, and enhance data security. Furthermore, we anticipate a growing integration of machine learning and data-driven optimization in tackling packet reordering. The insights provided aim to influence the future design and optimization of 6G networks, particularly concerning packet management and performance. This paper aims to assist researchers and practitioners in effectively leveraging packet reordering to promote efficient and secure operations of future 6G networks. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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