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Regeneration Control, Sensing and Digital Twin of Eco-Environment
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Regeneration Control, Sensing and Digital Twin of Eco-Environment

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

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 4036

Special Issue Editors

Prof. Dr. Baozhen Yao

E-Mail Website
Guest Editor
School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Interests: public transportation; vehicle automation; swarm intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yibin Ao

E-Mail Website
Guest Editor
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
Interests: travel behavior and energy effects using quantitative methods; rural infrastructure management
Special Issues, Collections and Topics in MDPI journals
Dr. Ronghui Zhang

E-Mail Website
Guest Editor
School of Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: intelligent transportation system, computer vision and intelligent control
Prof. Dr. Xing Zhu

E-Mail Website
Guest Editor
College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
Interests: intelligent IoTs; remote sensing

Special Issue Information

Dear Colleagues,

Nowadays, modern cities are gradually developing in the direction of intelligence and ecology. For the eco-environment, making the dynamic evolution of the eco-environment measurable, observable and controllable through digital twinning (DT) technology and constructing the cyber-physical systems (CPSs) of the eco-environment will become the construction goals of eco-friendly smart cities and villages in the future. These goals have been envisioned as effective measures to promote the digital and intelligent development of modern cities and new countryside areas, and build a regeneration control system for smart cities and villages. However, a challenging issue is being faced in harnessing the multi-source and multi-dimensional data perception and control of the eco-environment.

DT technology leverages machine learning and the Internet of Things (IoTs) to create digital replicas of objects and constantly update replicas with real-time data from sensors. Empowered by DT technology, we can dynamically predict and optimize the evolution of the eco-environment. Meanwhile, regeneration control technology, which aims to recover the energy that would have been dissipated, holds great promise for improving energy utilization efficiency and reducing energy waste. The objective of this Special Issue is to promote the construction of eco-friendly smart cities and villages using DT technology and regeneration control technology. Particular attention will be given to the following themes and areas; however, it should be stressed that a broad range of submissions are encouraged. Potential topics of interest include, but are not limited to, the following:

  • Monitoring and cyber-physical system for water, air and soil in eco-environment based on IoTs;
  • Construction of transportation environment based on DT technology;
  • Parallel simulation technology of regenerative braking control for intelligent connected vehicle;
  • Evaluation of transportation environment based on big data technology;
  • Intelligent monitoring technology for fire and smoke in transportation based on deep learning;
  • Stakeholder engagement in the construction of eco-friendly smart cities using regeneration control and digital twin technology;
  • Differences in the application of digital twin technology in urban and rural areas.

Prof. Dr. Baozhen Yao
Prof. Dr. Yibin Ao
Dr. Ronghui Zhang
Prof. Dr. Xing Zhu
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 (2 papers)

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Research

23 pages, 4899 KiB  
Article
Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
by Yikang Rui, Yan Zhao, Wenqi Lu and Can Wang
Sensors 2024, 24(1), 86; https://doi.org/10.3390/s24010086 - 23 Dec 2023
Viewed by 1070
Abstract
The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased [...] Read more.
The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field. Full article
(This article belongs to the Special Issue Regeneration Control, Sensing and Digital Twin of Eco-Environment)
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23 pages, 5977 KiB  
Article
Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles
by Wanting Gou, Haodi Zhang and Ronghui Zhang
Sensors 2023, 23(21), 8788; https://doi.org/10.3390/s23218788 - 28 Oct 2023
Cited by 7 | Viewed by 2083
Abstract
The Internet of Vehicles(IoV) employs vehicle-to-everything (V2X) technology to establish intricate interconnections among the Internet, the IoT network, and the Vehicle Networks (IVNs), forming a complex vehicle communication network. However, the vehicle communication network is very vulnerable to attacks. The implementation of an [...] Read more.
The Internet of Vehicles(IoV) employs vehicle-to-everything (V2X) technology to establish intricate interconnections among the Internet, the IoT network, and the Vehicle Networks (IVNs), forming a complex vehicle communication network. However, the vehicle communication network is very vulnerable to attacks. The implementation of an intrusion detection system (IDS) emerges as an essential requisite to ensure the security of in-vehicle/inter-vehicle communication in IoV. Within this context, the imbalanced nature of network traffic data and the diversity of network attacks stand as pivotal factors in IDS performance. On the one hand, network traffic data often heavily suffer from data imbalance, which impairs the detection performance. To address this issue, this paper employs a hybrid approach combining the Synthetic Minority Over-sampling Technique (SMOTE) and RandomUnderSampler to achieve a balanced class distribution. On the other hand, the diversity of network attacks constitutes another significant factor contributing to poor intrusion detection model performance. Most current machine learning-based IDSs mainly perform binary classification, while poorly dealing with multiclass classification. This paper proposes an adaptive tree-based ensemble network as the intrusion detection engine for the IDS in IoV. This engine employs a deep-layer structure, wherein diverse ML models are stacked as layers and are interconnected in a cascading manner, which enables accurate and efficient multiclass classification, facilitating the precise identification of diverse network attacks. Moreover, a machine learning-based approach is used for feature selection to reduce feature dimensionality, substantially alleviating the computational overhead. Finally, we evaluate the proposed IDS performance on various cyber-attacks from the in-vehicle and external networks in IoV by using the network intrusion detection dataset CICIDS2017 and the vehicle security dataset Car-Hacking. The experimental results demonstrate remarkable performance, with an F1-score of 0.965 on the CICIDS2017 dataset and an F1-score of 0.9999 on the Car-Hacking dataset. These scores demonstrate that our IDS can achieve efficient and precise multiclass classification. This research provides a valuable reference for ensuring the cybersecurity of IoV. Full article
(This article belongs to the Special Issue Regeneration Control, Sensing and Digital Twin of Eco-Environment)
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