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Internet of Vehicles
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Internet of Vehicles

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

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 56583

Special Issue Editors

Dr. Nicolas Hautière

E-Mail Website
Guest Editor
Département Composants et Systèmes, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux, 14-20 Boulevard Newton, Cité Descartes - Champs sur Marne, 77447 Marne la Vallée Cedex 2 - France
Interests: computer vision, image processing, intelligent transportation systems
Prof. Dr. Mohamed Mosbah

E-Mail Website
Guest Editor
LaBRI , Université de Bordeaux, 351, cours de la Libération, 33405 Talence Cedex, France
Interests: distributed algorithms; networks and protocols; wireless sensor networks; intelligent transportation systems; security and safety
Special Issues, Collections and Topics in MDPI journals
Dr. Imen Jemili

E-Mail Website
Guest Editor
Faculty of sciences of Bizerte, Univ. Carthage, 7021 Bizerte, Tunisia
Interests: wireless communications, smart cities, Internet of vehicles, routing, MAC protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first deployments at large scale of connected vehicles are happening throughout the world. Connected through ad-hoc networks, these vehicles are giving birth to an actual “Internet of Vehicles”, which builds the foundation of a next generation of traffic management systems.  

While day-one applications were relatively straightforward to implement, higher-level applications, like the monitoring of environmental and weather conditions and of the infrastructure and its equipment, depend both on the metrological characteristics of the in-vehicle sensors and on the computational architecture, as well as on the algorithms. However, the ability to implement such applications is a key issue, e.g. to enable the safe deployment of highly-automated vehicles, or to implement automated enforcement strategies in restricted zones.

This Special Issue welcomes contributions dealing with all the technological facets of the Internet of Vehicles, including architecture, communication technologies, advanced applications, sensing and algorithms, but also on deployment issues, such as the development of energy-efficient RSUs.

Dr. Nicolas Hautière
Prof. Mohamed Mosbah
Dr. Imen Jemili
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

  • IoV architectures and protocols 
  • Benchmarking of communication technologies in IoV 
  • Advanced IoV applications
  • Advanced sensing and measurement characterization for IoV applications 
  • Machine learning algorithms for IoV 
  • Sensor calibration for IoV 
  • Fog computing (edge computing) in IoV 
  • SmartX (cities; vehicles; platoons) 
  • Energy efficiency and road side units (energy harvesting; RF energy transfer) for IoV
  • Modelling and performance evaluation 
  • Deployment pilots and issues 
  • Safety, security and privacy in IoV 
  • Communication technologies (DSRC/WAVE, 5G, LTE, Wifi, ...) 
  • Protocols, architectures and applications for the Internet of Vehicles

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Published Papers (7 papers)

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Research

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28 pages, 3584 KiB  
Article
Hierarchical Network Architecture for Non-Safety Applications in Urban Vehicular Ad-Hoc Networks
by Sangsoo Jeong, Youngmi Baek and Sang Hyuk Son
Sensors 2019, 19(19), 4306; https://doi.org/10.3390/s19194306 - 4 Oct 2019
Cited by 3 | Viewed by 3029
Abstract
In the vehicular ad-hoc networks (VANETs), wireless access in vehicular environments (WAVE) as the core networking technology is suitable for supporting safety-critical applications, but it is difficult to guarantee its performance when transmitting non-safety data, especially high volumes of data, in a multi-hop [...] Read more.
In the vehicular ad-hoc networks (VANETs), wireless access in vehicular environments (WAVE) as the core networking technology is suitable for supporting safety-critical applications, but it is difficult to guarantee its performance when transmitting non-safety data, especially high volumes of data, in a multi-hop manner. Therefore, to provide non-safety applications effectively and reliably for users, we propose a hybrid V2V communication system (HVCS) using hierarchical networking architecture: a centralized control model for the establishment of a fast connection and a local data propagation model for efficient and reliable transmissions. The centralized control model had the functionality of node discovery, local ad-hoc group (LAG) formation, a LAG owner (LAGO) determination, and LAG management. The local data propagation indicates that data are transmitted only within the LAG under the management of the LAGO. To support the end-to-end multi-hop transmission over V2V communication, vehicles outside the LAG employ the store and forward model. We designed three phases consisting of concise device discovery (CDD), concise provisioning (CP), and data transmission, so that the HVCS is highly efficient and robust on the hierarchical networking architecture. Under the centralized control, the phase of the CDD operates to improve connection establishment time, and the CP is to simplify operations required for security establishment. Our HVCS is implemented as a two-tier system using a traffic controller for centralized control using cellular networks and a smartphone for local data propagation over Wi-Fi Direct. The HVCS’ performance was evaluated using Veins, and compared with WAVE in terms of throughput, connectivity, and quality of service (QoS). The effectiveness of the centralized control was demonstrated in comparative experiments with Wi-Fi Direct. The connection establishment time measured was only 0.95 s for the HVCS. In the case of video streaming services through the HVCS, about 98% of the events could be played over 16 frames per second. The throughput for the streaming data was between 74% to 81% when the vehicle density was over 50%. We demonstrated that the proposed system has high throughput and satisfies the QoS of streaming services even though the end-to-end delay is a bit longer when compared to that of WAVE. Full article
(This article belongs to the Special Issue Internet of Vehicles)
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21 pages, 4157 KiB  
Article
A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications
by Tianhong Zhang, Sheng Liu, Weidong Xiang, Limei Xu, Kaiyu Qin and Xiao Yan
Sensors 2019, 19(16), 3541; https://doi.org/10.3390/s19163541 - 13 Aug 2019
Cited by 20 | Viewed by 4063
Abstract
Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing [...] Read more.
Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing was extracted from experiments under several different road scenarios including highways, local areas, residential areas, state parks, and rural areas. The study shows that the proposed PL prediction model outperforms conventional empirical models. Meanwhile, the proposed PD prediction model achieves higher prediction accuracy than the statistical one. Moreover, the prediction model can operate in real-time, through updating its training set, to predict channel parameters. Such a model can be easily extended to the applications of autonomous driving, the Internet of Things (IoT), 5th generation cellular network technology (5G) and many others. Full article
(This article belongs to the Special Issue Internet of Vehicles)
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19 pages, 8230 KiB  
Article
A Novel Method to Enable the Awareness Ability of Non-V2V-Equipped Vehicles in Vehicular Networks
by Jian Wang, Qiang Zheng, Fang Mei, Weiwen Deng and Yuming Ge
Sensors 2019, 19(9), 2187; https://doi.org/10.3390/s19092187 - 11 May 2019
Cited by 1 | Viewed by 3529
Abstract
Autonomous vehicles need to have sufficient perception of the surrounding environment to produce appropriate driving behavior. The Vehicle-to-Vehicle (V2V) communication technology can exchange the speed, position, direction, and other information between autonomous vehicles to improve the sensing ability of the traditional on-board sensors. [...] Read more.
Autonomous vehicles need to have sufficient perception of the surrounding environment to produce appropriate driving behavior. The Vehicle-to-Vehicle (V2V) communication technology can exchange the speed, position, direction, and other information between autonomous vehicles to improve the sensing ability of the traditional on-board sensors. For example, V2V communication technology does not have a blind spot like a conventional on-board sensor, and V2V communication is not easily affected by weather conditions. However, it is almost impossible to make every vehicle a V2V-equipped vehicle in the real environment due to reasons such as policy and user choice. Low penetration of V2V-equipped vehicles greatly reduces the performance of the traditional V2V system. In this paper, however, we propose a novel method that can extend the awareness ability of the traditional V2V system without adding much extra investment. In the traditional V2V system, only a V2V-equipped vehicle can broadcast its own location information. However, the situation is somewhat different in our V2V system. Although non-V2V-equipped vehicles cannot broadcast their own location information, we can let V2V-equipped vehicle with radar and other sensors detect the location information of the surrounding non-V2V-equipped vehicles and then broadcast it out. Therefore, we think that a non-V2V-equipped vehicle can also broadcast its own location information. In this way, we greatly extend the awareness ability of the traditional V2V system. The proposed method is validated by real experiments and simulation experiments. Full article
(This article belongs to the Special Issue Internet of Vehicles)
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18 pages, 3581 KiB  
Article
Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach
by Chen Wang, Jacques Delport and Yan Wang
Sensors 2019, 19(9), 2111; https://doi.org/10.3390/s19092111 - 7 May 2019
Cited by 11 | Viewed by 3399
Abstract
Drivers’ behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short [...] Read more.
Drivers’ behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short time period. To overcome the limitations of making an immediate prediction, in this work, we propose a two-stage data-driven approach: classifying driving patterns of on-road surrounding vehicles using the Gaussian mixture models (GMM); and predicting vehicles’ short-term lateral motions (i.e., left/right turn and left/right lane change) based on real-world vehicle mobility data, provided by the U.S. Department of Transportation, with different ensemble decision trees. We considered several important kinetic features and higher order kinematic variables. The research results of our proposed approach demonstrate the effectiveness of pattern classification and on-road lateral motion prediction. This methodology framework has the potential to be incorporated into current data-driven collision warning systems, to enable more practical on-road preprocessing in intelligent vehicles, and to be applied in autopilot-driving scenarios. Full article
(This article belongs to the Special Issue Internet of Vehicles)
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17 pages, 7904 KiB  
Article
A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data
by Jun Zhang, ZhongCheng Wu, Fang Li, Chengjun Xie, Tingting Ren, Jie Chen and Liu Liu
Sensors 2019, 19(6), 1356; https://doi.org/10.3390/s19061356 - 18 Mar 2019
Cited by 91 | Viewed by 10015
Abstract
Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can [...] Read more.
Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods. This paper proposes an end-to-end deep learning framework by fusing convolutional neural networks and recurrent neural networks with an attention mechanism, which is more suitable for time series CAN-BUS sensor data. The proposed method can automatically learn features of driving behaviors and model temporal features without professional knowledge in features modeling. Moreover, the method can capture salient structure features of high-dimensional sensor data and explore the correlations among multi-sensor data for rich feature representations of driving behaviors. Experimental results show that the proposed framework performs well in the real world driving behavior identification task, outperforming the state-of-the-art methods. Full article
(This article belongs to the Special Issue Internet of Vehicles)
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Review

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29 pages, 4020 KiB  
Review
Review on V2X, I2X, and P2X Communications and Their Applications: A Comprehensive Analysis over Time
by José Manuel Lozano Domínguez and Tomás Jesús Mateo Sanguino
Sensors 2019, 19(12), 2756; https://doi.org/10.3390/s19122756 - 19 Jun 2019
Cited by 58 | Viewed by 11008
Abstract
Smart cities are ecosystems where novel ideas and emerging technologies meet to improve economy, environment, governance, living, and mobility. One of the pillars of smart cities is transport, with the improvement of mobility and the reduction of traffic accidents being some of the [...] Read more.
Smart cities are ecosystems where novel ideas and emerging technologies meet to improve economy, environment, governance, living, and mobility. One of the pillars of smart cities is transport, with the improvement of mobility and the reduction of traffic accidents being some of the current key challenges. With this purpose, this manuscript reviews the state-of-the-art of communications and applications in which different actors of the road are involved. Thus, the objectives of this survey are intended to determine who, when, and about what is being researched around smart cities. Particularly, the goal is to situate the focus of scientific and industrial progress on V2X, I2X, and P2X communication to establish a taxonomy that reduces ambiguous acronyms around the communication between vehicles, infrastructure, and pedestrians, as well as to determine what the trends and future technologies are that will lead to more powerful applications. To this end, this literature review article presents a comprehensive study including a representative collection of the 100 most cited papers and patents published in the literature together with a statistical bibliometric analysis of 14,364 keywords over 3422 contributions between 1997 and 2018. As a result, this work provides a technological profile considering different dimensions along the paper, such as the type of communication, use case, country, organization, terminology, and year. Full article
(This article belongs to the Special Issue Internet of Vehicles)
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20 pages, 2245 KiB  
Review
A Survey of Vehicle to Everything (V2X) Testing
by Jian Wang, Yameng Shao, Yuming Ge and Rundong Yu
Sensors 2019, 19(2), 334; https://doi.org/10.3390/s19020334 - 15 Jan 2019
Cited by 220 | Viewed by 20147
Abstract
Vehicle to everything (V2X) is a new generation of information and communication technologies that connect vehicles to everything. It not only creates a more comfortable and safer transportation environment, but also has much significance for improving traffic efficiency, and reducing pollution and accident [...] Read more.
Vehicle to everything (V2X) is a new generation of information and communication technologies that connect vehicles to everything. It not only creates a more comfortable and safer transportation environment, but also has much significance for improving traffic efficiency, and reducing pollution and accident rates. At present, the technology is still in the exploratory stage, and the problems of traffic safety and information security brought about by V2X applications have not yet been fully evaluated. Prior to marketization, we must ensure the reliability and maturity of the technology, which must be rigorously tested and verified. Therefore, testing is an important part of V2X technology. This article focuses on the V2X application requirements and its challenges, the need of testing. Then we also investigate and summarize the testing methods for V2X in the communication process and describe them in detail from the architectural perspective. In addition, we have proposed an end-to-end testing system combining virtual and real environments which can undertake the test task of the full protocol stack. Full article
(This article belongs to the Special Issue Internet of Vehicles)
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