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Human Digital Twins for Industry 5.0: Current Perspectives and Future Directions

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

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 3181

Special Issue Editors

School of Computer Science and Information Technology, University College Cork, T12 R229 Cork, Ireland
Interests: digital twins; blockchain; Industry 4.0/5.0; smart manufacturing; Healthcare 4.0/5.0; IoT; big data; stream processing; collaborative systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science and Information Technology, University College Cork, College Road, Cork, Ireland
Interests: Artificial Intelligence; optimisation; sensor networking; smart cities

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Guest Editor
Insight Centre for Data Analysis, University of Galway, Galway, Ireland
Interests: Industry 4.0/5.0 blockchain multi-robot collaboration; space robotic; federated learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The term “digital twin” was first coined by NASA for a virtual replica of a physical structure in real space. Digital twin technology plays a vital role in the Industrial Revolution, leading to more personalized, intelligent, and proactive collaboration with machines. Industry 5.0 is a human-centric industrial revolution aiming to leverage human experts’ creativity in collaboration with efficient, intelligent and accurate machines by empowering the human-in-the-loop (HITL) design model. Most research and industry have recently adopted digital twin technology for human-centric advances based on the Industry 5.0 paradigm for product personalization and customization. Moreover, they have started to integrate digital twin technology with emerging industrial technologies (e.g., nanotechnology, 5G technologies, drone technology, blockchain, DTs, robotics, big data, IoT, AI, and cloud computing) to support applications that allow humans and machines to work hand in hand. Consequently, a human digital twin could be defined for a specific purpose to implement Industry 5.0 applications and use cases. However, the human digital twin model still faces challenges due to privacy, rights, regulations and ethics. Although there has been significant progress toward digital twins in industries, more research innovation, dissemination and technologies are needed to unbundle new opportunities and move towards human personal digital twins to empower the Industry 5.0 vision.

This Special Issue targets exploring new research directions in human digital twins in combination with Industry 5.0 technologies. The aim is to document the current state-of-the-art and identify future directions in human digital twins for research work in Industry 5.0. This Special Issue is also designed to highlight the applications, industrial experiments, studies and use cases of human digital twins for Industry 5.0.

Therefore, the suggested topics of interest for the Special Issue include, but are not limited to: 

  • Human digital twin-based frameworks and solutions for Industry 5.0
  • Human digital twins solutions for product personalization and customization
  • Data privacy , ethics and regulations for human/personal digital twin
  • Modelling for human/personal digital twins
  • IoT and human/personal digital twins
  • Blockchain for human digital twins collaboration
  • Digital human and robot collaboration
  • Real-time system prediction based on human/personal digital twins
  • Decentralization for human digital twins collaboration
  • Human digital twins for smart decision
  • Predictive models based on human digital twins
  • Distributed machine learning/Federated learning/Ensemblelearning for human digital twins solutions
  • Explainable AI for decision making based on human/personal digital twins
  • Personal digital twins for Healthcare 5.0 and Society 5.0
  • Human digital twins for Factory 5.0, Production 5.0 .etc

Dr. Radhya Sahal
Prof. Dr. Kenneth Brown
Dr. Saeed Alsamhi
Guest Editors

Manuscript Submission Information

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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

  • human digital twins
  • Industry 5.0
  • human-in-the-loop
  • human-centric
  • data analysis
  • machine/deep learning
  • federated learning
  • IoT
  • blockchain
  • Healthcare 5.0

Published Papers (1 paper)

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Research

26 pages, 712 KiB  
Article
Enabling Trust and Security in Digital Twin Management: A Blockchain-Based Approach with Ethereum and IPFS
by Austine Onwubiko, Raman Singh , Shahid Awan , Zeeshan Pervez and Naeem Ramzan 
Sensors 2023, 23(14), 6641; https://doi.org/10.3390/s23146641 - 24 Jul 2023
Cited by 4 | Viewed by 1853
Abstract
The emergence of Industry 5.0 has highlighted the significance of information usage, processing, and data analysis when maintaining physical assets. This has enabled the creation of the Digital Twin (DT). Information about an asset is generated and consumed during its entire life cycle. [...] Read more.
The emergence of Industry 5.0 has highlighted the significance of information usage, processing, and data analysis when maintaining physical assets. This has enabled the creation of the Digital Twin (DT). Information about an asset is generated and consumed during its entire life cycle. The main goal of DT is to connect and represent physical assets as close to reality as possible virtually. Unfortunately, the lack of security and trust among DT participants remains a problem as a result of data sharing. This issue cannot be resolved with a central authority when dealing with large organisations. Blockchain technology has been proposed as a solution for DT information sharing and security challenges. This paper proposes a Blockchain-based solution for digital twin using Ethereum blockchain with performance and cost analysis. This solution employs a smart contract for information management and access control for stakeholders of the digital twin, which is secure and tamper-proof. This implementation is based on Ethereum and IPFS. We use IPFS storage servers to store stakeholders’ details and manage information. A real-world use-case of a production line of a smartphone, where a conveyor belt is used to carry different parts, is presented to demonstrate the proposed system. The performance evaluation of our proposed system shows that it is secure and achieves performance improvement when compared with other methods. The comparison of results with state-of-the-art methods showed that the proposed system consumed fewer resources in a transaction cost, with an 8% decrease. The execution cost increased by 10%, but the cost of ether was 93% less than the existing methods. Full article
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