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Enhancing the Adoption and Usage of Advanced Sensing Technologies in Industry 4.0

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

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 11327

Special Issue Editors

Prof. Dr. Guochao Peng

E-Mail Website
Guest Editor
School of Information Management, Sun Yat-sen University, Guangzhou 510006, China
Interests: Internet of Things; smart manufacturing; smart city; artificial intelligence; user behavior; big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Fei Xing

E-Mail Website
Guest Editor
School of E-Commerce and Logistics, Suzhou Institute of Trade and Commerce, Suzhou 215009, China
Interests: information system; smart manufacturing; smart healthcare; user behavior

Special Issue Information

Dear Colleagues,

In the era of the 4th Industrial Revolution, Internet of Things (IoT) and advanced sensing technologies have been increasingly adopted and used in a wide range of socio-technical scenes, from smart factories to smart cities, healthcare, transportation, energy supply, agriculture, public surveillance and cultural heritage, to name a few. On one hand, these innovative IoT and sensing technologies lay the infrastructural foundation of digital transformation; on the other hand, they raise new challenges and issues related to their security, stability, accuracy and reliability.

As such, this Special Issue provides an international platform for researchers to share their latest research, theories and concerns regarding the adoption and usage of advanced sensing technologies in diverse socio-technical scenes of the 4th Industrial Revolution. We also welcome contributions and validated solutions using innovative methods, designs, protocols and mechanisms to enhance the security, stability, accuracy and reliability of sensors and IoT networks.

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

  • Adoption and usage of advanced sensing technologies across the product life cycle in smart manufacturing;
  • Challenges and issues associated with the adoption of IoT and advanced sensing technologies in smart manufacturing;
  • Advanced sensing technologies and IoT applications to support digital twins and industrial simulations;
  • Methods, designs, protocols and mechanisms to enhance the security, stability, accuracy and reliability of sensors and IoT networks;
  • Adoption, testbeds, models and applications of IoT and advanced sensing technologies in smart cities;
  • Theories, methods, models, issues and applications of IoT and advanced sensing technologies in related socio-technical scenes (e.g., healthcare, transportation, energy supply, agriculture, public surveillance, and cultural heritage) in the 4th Industrial Revolution.

Prof. Dr. Guochao Peng
Dr. Fei Xing
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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16 pages, 3361 KiB  
Article
AI-Guided Computing Insights into a Thermostat Monitoring Neonatal Intensive Care Unit (NICU)
by Ning Zhang, Olivia Wood, Zhiyin Yang and Jianfei Xie
Sensors 2023, 23(9), 4492; https://doi.org/10.3390/s23094492 - 5 May 2023
Cited by 2 | Viewed by 4046
Abstract
In any healthcare setting, it is important to monitor and control airflow and ventilation with a thermostat. Computational fluid dynamics (CFD) simulations can be carried out to investigate the airflow and heat transfer taking place inside a neonatal intensive care unit (NICU). In [...] Read more.
In any healthcare setting, it is important to monitor and control airflow and ventilation with a thermostat. Computational fluid dynamics (CFD) simulations can be carried out to investigate the airflow and heat transfer taking place inside a neonatal intensive care unit (NICU). In this present study, the NICU is modeled based on the realistic dimensions of a single-patient room in compliance with the appropriate square footage allocated per incubator. The physics of flow in NICU is predicted based on the Navier–Stokes conservation equations for an incompressible flow, according to suitable thermophysical characteristics of the climate. The results show sensible flow structures and heat transfer as expected from any indoor climate with this configuration. Furthermore, machine learning (ML) in an artificial intelligence (AI) model has been adopted to take the important geometric parameter values as input from our CFD settings. The model provides accurate predictions of the thermal performance (i.e., temperature evaluation) associated with that design in real time. Besides the geometric parameters, there are three thermophysical variables of interest: the mass flow rate (i.e., inlet velocity), the heat flux of the radiator (i.e., heat source), and the temperature gradient caused by the convection. These thermophysical variables have significantly recovered the physics of convective flows and enhanced the heat transfer throughout the incubator. Importantly, the AI model is not only trained to improve the turbulence modeling but also to capture the large temperature gradient occurring between the infant and surrounding air. These physics-informed (Pi) computing insights make the AI model more general by reproducing the flow of fluid and heat transfer with high levels of numerical accuracy. It can be concluded that AI can aid in dealing with large datasets such as those produced in NICU, and in turn, ML can identify patterns in data and help with the sensor readings in health care. Full article
(This article belongs to the Special Issue Enhancing the Adoption and Usage of Advanced Sensing Technologies in Industry 4.0)
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Review

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15 pages, 841 KiB  
Review
Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream
by Igor G. Olaizola, Marco Quartulli, Elias Unzueta, Juan I. Goicolea and Julián Flórez
Sensors 2022, 22(23), 9164; https://doi.org/10.3390/s22239164 - 25 Nov 2022
Cited by 10 | Viewed by 6468
Abstract
Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the “fourth industrial revolution” that must be adapted to the particular context [...] Read more.
Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the “fourth industrial revolution” that must be adapted to the particular context and needs of big continuous processes such as oil refineries that have evolved to control paradigms supported by sector-specific technologies where big volumes of operation-driven data are continuously captured from a plethora of sensors. The introduction of Artificial Intelligence techniques can overcome the current limitations of Advanced Control Systems (mainly MPCs) by providing better performance on highly non-linear and complex systems and by operating with a broader scope in terms of signals/data and sub-systems. Moreover, the state of the art of traditional PID/MPC based solutions is showing an asymptotic improvement that requires a disruptive approach in order to reach relevant improvements in terms of efficiency, optimization, maintenance, etc. This paper shows the key aspects in oil refineries to successfully adopt Big Data and Machine Learning solutions that can significantly improve the efficiency and competitiveness of continuous processes. Full article
(This article belongs to the Special Issue Enhancing the Adoption and Usage of Advanced Sensing Technologies in Industry 4.0)
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