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Symmetry
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Selected Papers from The 2023 International Conference on Digital Twin...
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Selected Papers from The 2023 International Conference on Digital Twin (Digital Twin 2023)

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 5675

Special Issue Editors

Dr. Sahraoui Dhelim

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland
Interests: social computing; IoT; machine learning; blockchain; edge computing; VANET
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Zhu

E-Mail Website
Guest Editor
School of Computer science, University of South China, Hengyang 421001, China
Interests: IoT; pervasive computing; assisted living and evolutionary computation
Special Issues, Collections and Topics in MDPI journals
Dr. Nyothiri Aung

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, Dublin, Ireland
Interests: medical image analysis; intelligent transportation systems; IoT; social networks analysis; mobile edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2023 International Conference on Digital Twin (Digital Twin 2023) will take place in Portsmouth, England, on 28–31 August 2023.

The 2023 IEEE International Conference on Digital Twin will provide a high-profile, leading-edge forum for researchers, engineers, and practitioners to present state-of-art advances and innovations on Digital Twins, as well as to identify emerging research topics and define the future of the field.

Track 1: Digital Twin Fundamentals

  • Concepts and theories;
  • Architecture and frameworks;
  • Methodology, lifecycle, and processes;
  • Data-driven-based modelling and technologies;
  • Simulation-based approaches and technologies;
  • Multi-physics and multi-scale simulation;
  • Model-based system engineering;
  • Computational dynamic modelling;
  • Process and workflow modelling and simulation;
  • Dynamic prediction and projection.

Track 2: Digital Twin Interaction and Communication

  • Physical and virtual twin communication;
  • Physical and virtual twin interaction;
  • Distributed digital twin systems;
  • Digital twin interoperability;
  • Cooperative and collaborative digital twins;
  • Digital twin networks and organisation;
  • Distributed machine learning for digital twins;
  • Twins and user interactions;
  • User interfaces for digital twins.

Track 3: Digital Twin Security and Privacy

  • Threat modelling in digital twins;
  • Cybersecurity for digital twins;
  • Cross-layer defence for digital twins;
  • Digital twins for cybersecurity;
  • Privacy protection in digital twins;
  • Blockchain-based digital twins;
  • Digital-twin-based intrusion detection systems;
  • Digital-twin-based intrusion response systems.

Track 4: Digital Twin Systems and Applications

  • Development tools and platforms;
  • Standardization and regulation;
  • Servitisation of digital twins;
  • Automation and manufacturing;
  • Digital health and assisted systems;
  • Smart cities and transportation;
  • Automotives and aerospace;
  • Sustainable energy.

For more information, please visit the conference website: https://ieee-smart-world-congress.org/program/digitaltwin2023/overview

All published papers in the Special Issue belong to the journal of Symmetry, so the papers must fit both the scope of Symmetry and the conference.

Dr. Sahraoui Dhelim
Dr. Tao Zhu
Dr. Nyothiri Aung
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. Symmetry is an international peer-reviewed open access monthly 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.

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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29 pages, 11919 KiB  
Article
Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization
by Vijayalakshmi K. Kumarasamy, Abhilasha Jairam Saroj, Yu Liang, Dalei Wu, Michael P. Hunter, Angshuman Guin and Mina Sartipi
Symmetry 2024, 16(4), 448; https://doi.org/10.3390/sym16040448 - 7 Apr 2024
Cited by 3 | Viewed by 2228
Abstract
Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution [...] Read more.
Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system. Full article
(This article belongs to the Special Issue Selected Papers from The 2023 International Conference on Digital Twin (Digital Twin 2023))
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23 pages, 6327 KiB  
Article
Digital Twin Prototypes for Supporting Automated Integration Testing of Smart Farming Applications
by Alexander Barbie, Wilhelm Hasselbring and Malte Hansen
Symmetry 2024, 16(2), 221; https://doi.org/10.3390/sym16020221 - 12 Feb 2024
Cited by 3 | Viewed by 1901
Abstract
Industry 4.0 marks a major technological shift, revolutionizing manufacturing with increased efficiency, productivity, and sustainability. This transformation is paralleled in agriculture through smart farming, employing similar advanced technologies to enhance agricultural practices. Both fields demonstrate a symmetry in their technological approaches. Recent advancements [...] Read more.
Industry 4.0 marks a major technological shift, revolutionizing manufacturing with increased efficiency, productivity, and sustainability. This transformation is paralleled in agriculture through smart farming, employing similar advanced technologies to enhance agricultural practices. Both fields demonstrate a symmetry in their technological approaches. Recent advancements in software engineering and the digital twin paradigm are addressing the challenge of creating embedded software systems for these technologies. Digital twins allow full development of software systems before physical prototypes are made, exemplifying a cost-effective method for Industry 4.0 software development. Our digital twin prototype approach mirrors software operations within a virtual environment, integrating all sensor interfaces to ensure accuracy between emulated and real hardware. In essence, the digital twin prototype acts as a prototype of its physical counterpart, effectively substituting it for automated testing of physical twin software. This paper discusses a case study applying this approach to smart farming, specifically enhancing silage production. We also provide a lab study for independent replication of this approach. The source code for a digital twin prototype of a PiCar-X by SunFounder is available open-source on GitHub, illustrating how digital twins can bridge the gap between virtual simulations and physical operations, highlighting the symmetry between physical and digital twins. Full article
(This article belongs to the Special Issue Selected Papers from The 2023 International Conference on Digital Twin (Digital Twin 2023))
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