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Sustainability
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Intelligent Sensing, Control and Optimization for Sustainable Cyber-Physical Systems
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Intelligent Sensing, Control and Optimization for Sustainable Cyber-Physical Systems

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (13 September 2023) | Viewed by 1867

Special Issue Editors

Prof. Dr. Li Zhu

E-Mail Website
Guest Editor
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100082, China
Interests: communication based train control; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Meng Li

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100082, China
Interests: wireless network; machine learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable cyber-physical systems refer to next-generation networked systems whose normal functioning largely relies on the cyber-physical interactions among neighbored cyber-physical individuals. Many modern critical infrastructures can be modeled as sustainable CPS. Typical examples of such systems include power grids, the Internet, industrial sensor networks and public transportation systems. Advances in sensing, communication and control technology open opportunities for the implementation of various intelligent sensing, control and optimization strategies for CPS. Within this context, many advantages can be yielded from CPS compared with the traditional networked systems (including both complex networks and multi-agent systems), such as a high precision sensing and control ability, powerful on-line optimization ability, favorable fault-tolerant capability and high scalability. However, many issues need to be addressed before all the above-mentioned advantages can be realized.

This Special Issue aims to establish a forum for international researchers from different fields of electrical engineering, systems and control theory, computer science and applied mathematics, to present and evaluate the most recent developments and new ideas in the intelligent sensing, control and optimization of sustainable CPS, regarding both fundamental theory and practical applications.

Potential topics include, but are not limited to the following:

  • Condition monitoring of key equipment in sustainable CPS;
  • Advanced sensing solutions for sustainable CPS;
  • Multimodal sensor fusion and data analysis;
  • Real-time process monitoring for sustainable CPS;
  • Process optimization and control of sustainable CPS;
  • Digital twin-assisted sustainable CPS;
  • Prognostics and health management (PHM) applications in sustainable CPS;
  • Real-time and reliable communication in sustainable CPS;
  • Sensing and communication security in sustainable CPS;
  • Adaptive control and optimization in sustainable CPS;
  • Hybrid computing for real-time monitoring and control in sustainable CPS.

I/We look forward to receiving your contributions.

Prof. Dr. Li Zhu
Dr. Meng Li
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. Sustainability 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

  • AI-based sensing
  • intelligent control and communication
  • AI-based optimization
  • digital twin
  • machine learning
  • cyber-physical systems

Published Papers (1 paper)

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Research

18 pages, 2696 KiB  
Article
Vehicle Intersections Prediction Based on Markov Model with Variable Weight Optimization
by Zhihui He, Lei Ning, Baihui Jiang, Jiajia Li and Xin Wang
Sustainability 2023, 15(8), 6943; https://doi.org/10.3390/su15086943 - 20 Apr 2023
Cited by 1 | Viewed by 1257
Abstract
In this study, a new algorithm for predicting vehicle turning at intersections is proposed. The method is based on the Markov chain and can predict vehicle trajectories using GPS location sequences. Unlike traditional Markov models, which use preset weights, we created the Markov [...] Read more.
In this study, a new algorithm for predicting vehicle turning at intersections is proposed. The method is based on the Markov chain and can predict vehicle trajectories using GPS location sequences. Unlike traditional Markov models, which use preset weights, we created the Markov model using a data-driven weight selection method. The proposed model can dynamically adjust the weights of each intersection’s influence on current trajectories based on the data, in contrast to the fixed weights in traditional models. The study also details how to process trajectory data to identify whether a vehicle has passed through an intersection and how to determine the adjacency relationship of intersections, thus providing a reference for implementing a model of the classification problem. The data-driven algorithm was applied and compared to the fixed-weight algorithm on the same trajectory dataset, and the superiority of the weight selection algorithm was proven. The prediction accuracy of the traditional method was 49.61%, while the proposed method achieved a prediction accuracy of 60.66% for 100,000 trajectory datasets, nearly an 11% increase. Volunteer participation in the second dataset collected on the university campus showed that the accuracy of the proposed method could be further improved to 79.31% as the GPS sampling frequency increased. Simulation results show that the algorithm provides accurate prediction and that the prediction effect is improved with the expansion of the trajectory data set and the increase in GPS sampling frequency. The proposed algorithm has the potential to provide a location-based optimization of network resource allocation. Full article
(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization for Sustainable Cyber-Physical Systems)
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