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Cloud-Edge-Device Collaboration Computing on Internet of Vehicles and Intelligent Connected Vehicle

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 8519

Special Issue Editors

Prof. Dr. Jie Cui

E-Mail Website
Guest Editor
School of Computer Science and Technology, Anhui University, Hefei 230039, China
Interests: vehicular ad hoc network; applied cryptography; IoT security
Special Issues, Collections and Topics in MDPI journals
Dr. P. Vijayakumar

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University College of Engineering Tindivanam, Tamilnadu 604 001, India
Interests: signal processing; machine learning; deep learning; cognitive radio and intelligent system
Dr. Jiujun Cheng

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Interests: intelligent transportation; traffic engineering computing; traffic information systems; object detection; support vector machines
Prof. Dr. Lu Liu

E-Mail Website
Guest Editor
School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: data analytics; AI; cloud computing; service computing; IoT; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rising number of terminal devices, the computing ability of edges is becoming more and more powerful. However, this might reduce the computational efficiency due to some sudden events such as users’ random access, unknown movement of devices, etc. The collaboration among the cloud center, edge servers and end devices is becoming one of the hot topics on the Internet of Vehicles. It is valuable to explore the cooperation methods among the cloud, edge servers and single ending facilities. Although many scholars have conducted a lot of research on this topic, it is necessary to pay more attention to data privacy, task assignment, task offloading, resource combination and so on.

This Special Issue aims to provide a forum for the dissemination of recent advances in research and development in areas related to safe communication, data privacy, resource distribution and co-working strategy on the Internet of Vehicles and their integration with Artificial Intelligence Internet of Things (AIoT), simulation computing, etc. We are interested in engineering and data science solutions to the Internet of Vehicles, including collaboration algorithms between cloud and edge, intelligent machine learning models based on AIoT, data mining among edge networks, edge simulation computing and so on.

This Special Issue will solicit high-quality submissions from worldwide researchers who are active in the areas of knowledge engineering, machine learning, information fusion and data privacy, safe communication, edge resource scheduling optimization, AIoT simulation models or data management for IoV. Overall, we are interested in receiving papers on topics that include, but are not limited to:

  • Collaboration algorithms among cloud, edges and devices;
  • Engineering applications on the Internet of Vehicles;
  • Intelligent systems for the Internet of Vehicles;
  • Simulation of AIoT systems related to IoV;
  • Data privacy and secure communication for IoV;
  • Data transmission protocol between V2V and V2I;
  • Data collecting, processing and management for IoV;
  • Machine learning applications on IoV.

Prof. Dr. Jie Cui
Dr. P. Vijayakumar
Dr. Jiujun Cheng
Prof. Dr. Lu Liu
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 (3 papers)

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Research

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22 pages, 8055 KiB  
Article
A Data-Driven Noninteractive Authentication Scheme for the Internet of Vehicles in Mobile Heterogeneous Networks
by Zongzheng Wang, Ping Dong, Yuyang Zhang and Hongke Zhang
Sensors 2022, 22(22), 8623; https://doi.org/10.3390/s22228623 - 9 Nov 2022
Viewed by 1820
Abstract
The rapid development of intelligent vehicle networking technology has posed new requirements for in-vehicle gateway authentication security in the heterogeneous Internet of Vehicles (IoV). The current research on network layer authentication mechanisms usually relies on PKI infrastructure and interactive key agreement protocols, which [...] Read more.
The rapid development of intelligent vehicle networking technology has posed new requirements for in-vehicle gateway authentication security in the heterogeneous Internet of Vehicles (IoV). The current research on network layer authentication mechanisms usually relies on PKI infrastructure and interactive key agreement protocols, which have poor support for mobile and multihomed devices. Due to bandwidth and interaction delay overheads, they are not suitable for heterogeneous IoV scenarios with network state fluctuations. In this study, we propose a data-driven noninteractive authentication scheme, a lightweight, stateless scheme supporting mobility and multihoming to meet the lightweight data security requirements of the IoV. Our scheme implements device authentication and noninteractive key agreement through context parameters during data communication. Due to saving the signaling interactive delay and certificate overhead, in the IoV scenario, the proposed scheme reduced the delay by 20.1% and 11.8%, respectively, in the authentication and handover processes and brought higher bandwidth aggregation efficiency. Full article
(This article belongs to the Special Issue Cloud-Edge-Device Collaboration Computing on Internet of Vehicles and Intelligent Connected Vehicle)
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22 pages, 17176 KiB  
Article
Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network
by Di Zang, Yongjie Ding, Xiaoke Qu, Chenglin Miao, Xihao Chen, Junqi Zhang and Keshuang Tang
Sensors 2022, 22(7), 2744; https://doi.org/10.3390/s22072744 - 2 Apr 2022
Cited by 1 | Viewed by 2308
Abstract
Traffic-data recovery plays an important role in traffic prediction, congestion judgment, road network planning and other fields. Complete and accurate traffic data help to find the laws contained in the data more efficiently and effectively. However, existing methods still have problems to cope [...] Read more.
Traffic-data recovery plays an important role in traffic prediction, congestion judgment, road network planning and other fields. Complete and accurate traffic data help to find the laws contained in the data more efficiently and effectively. However, existing methods still have problems to cope with the case when large amounts of traffic data are missed. As a generalization of vector algebra, geometric algebra has more powerful representation and processing capability for high-dimensional data. In this article, we are thus inspired to propose the geometric-algebra-based generative adversarial network to repair the missing traffic data by learning the correlation of multidimensional traffic parameters. The generator of the proposed model consists of a geometric algebra convolution module, an attention module and a deconvolution module. Global and local data mean squared errors are simultaneously applied to form the loss function of the generator. The discriminator is composed of a multichannel convolutional neural network which can continuously optimize the adversarial training process. Real traffic data from two elevated highways are used for experimental verification. Experimental results demonstrate that our method can effectively repair missing traffic data in a robust way and has better performance when compared with the state-of-the-art methods. Full article
(This article belongs to the Special Issue Cloud-Edge-Device Collaboration Computing on Internet of Vehicles and Intelligent Connected Vehicle)
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Review

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25 pages, 14377 KiB  
Review
Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles
by Yu Zhu, Jian Wang, Fanqiang Meng and Tongtao Liu
Sensors 2022, 22(20), 7735; https://doi.org/10.3390/s22207735 - 12 Oct 2022
Cited by 10 | Viewed by 3629
Abstract
The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which [...] Read more.
The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which has large costs, lengthy durations, and few diverse test scenarios, cannot satisfy the autonomous driving system’s need for reliable and safe testing. Functional testing is the emphasis of the test since features such as frontal collision and traffic sign warning influence driving safety. As a result, simulation testing will undoubtedly emerge as a new technique for unmanned vehicle testing. A crucial aspect of simulation testing is the creation of test scenarios. With an emphasis on the map generating method and the dynamic scenario production method in the test scenarios, this article explains many scenarios and scenario construction techniques utilized in the process of self-driving car testing. A thorough analysis of the state of relevant research is conducted, and approaches for creating common scenarios as well as brand-new methods based on machine learning are emphasized. Full article
(This article belongs to the Special Issue Cloud-Edge-Device Collaboration Computing on Internet of Vehicles and Intelligent Connected Vehicle)
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