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Applied Sciences
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Digital Twin and IoT
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Digital Twin and IoT

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 4911

Special Issue Editors

Dr. Samuel D. Okegbile

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Network Intelligence and Innovation Laboratory, Concordia University, Montreal, QC, Canada
Interests: digital twin; Internet of Things; data sharing; artificial intelligence; wireless communication networks; blockchain
Dr. Fabrizio Marozzo

E-Mail Website
Guest Editor
Department of Informatics, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria, 87036 Rende, Italy
Interests: big data analysis; social media analysis; cloud computing; data mining; machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Dan García Carrillo

E-Mail Website
Guest Editor
Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain
Interests: computer networks security; IT security; Internet of Things; IT services; computer communications (Networks); LPWAN
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital twin (DT) is a powerful concept for the Internet of Things (IoT), offering substantial benefits across various industries such as manufacturing, healthcare, transportation, and more. By creating virtual replicas or representations of physical objects, processes, or systems, DT can significantly enhance both the development and operational phases of IoT networks. DT can facilitate real-time monitoring of physical entities or systems by continuously collecting data from embedded sensors, thus providing immediate insights into their behaviour, performance, and condition. This capability can enable proactive maintenance, predictive analytics, and operational optimization. Moreover, DTs can allow for simulation and what-if analysis of physical entities and systems, identifying inefficiencies, optimizing processes, and improving resource utilization while predicting equipment failures or maintenance needs before they occur.

Despite many benefits that can result from incorporating DT technology into IoT (DT-IoT), there are still issues and challenges to be addressed, including data security and privacy, data quality and integrity, interoperability, scalability, energy efficiency, cost, and ethical concerns. In this Special Issue, we aim to bring together high-quality contributions that address the aforementioned challenges. Topics covered will include, but are not limited to, the following:

  • Architectures and standards for DT-IoT;
  • Security and privacy solutions for DT-IoT;
  • Machine learning and AI for DT-IoT;
  • Communications issues in DT-IoT;
  • Societal and ethical aspects of DT-IoT;
  • Resource allocation and optimization;
  • Implementation challenges;
  • Service caching and DT mobility management.

Dr. Samuel D. Okegbile
Dr. Fabrizio Marozzo
Dr. Dan García Carrillo
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. Applied Sciences 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

  • digital twin
  • Internet of Things
  • data security
  • privacy
  • interoperability
  • scalability
  • machine learning
  • artificial intelligence
  • ethical concerns
  • optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

Jump to: Review

16 pages, 8596 KiB  
Article
Multi-Objective Optimization of Building Ventilation Systems Using Model Predictive Control: Integrating Air Quality, Energy Cost, and Environmental Impact
by Andreas Hyrup Andersen and Muhyiddine Jradi
Appl. Sci. 2025, 15(1), 451; https://doi.org/10.3390/app15010451 - 6 Jan 2025
Viewed by 750
Abstract
This paper presents a flexible heating, ventilation and air conditioning (HVAC) modeling framework developed for building digital twin implementation. The framework is showcased for the modeling and simulation of four ventilation systems in a 8500 m2 university building. The developed model includes [...] Read more.
This paper presents a flexible heating, ventilation and air conditioning (HVAC) modeling framework developed for building digital twin implementation. The framework is showcased for the modeling and simulation of four ventilation systems in a 8500 m2 university building. The developed model includes multiple objective model predictive control (MPC) with three objectives: electricity cost, indoor air quality and CO2 emission attributed to electricity consumption. A control strategy comparison is conducted between several MPC solutions with different objective weightings and a rule-based control strategy, which emulates the current system control. A novel approach for air quality evaluation is proposed and used for the MPC modeling and strategy comparison in this study. In this comparison, a “balanced” MPC strategy reduces energy costs by 18% compared to rule-based control while also providing significantly better air quality. An economic strategy achieves 24% savings with some air quality reduction, while an air-quality-focused strategy provides nearly “perfect” air quality with 11% savings. Finally, an environmental strategy shows the potential for prioritizing CO2 emissions over electricity costs. In this way, the strategy comparison illustrates the potential of MPC for the efficient operation and flexible objective prioritization according to stakeholder interests. Full article
(This article belongs to the Special Issue Digital Twin and IoT)
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Figure 1

Figure 1
<p>Component overview for typical HVAC system (<b>left</b>) and the considered ventilation system (<b>right</b>).</p> Full article ">Figure 2
<p>Components connections, and step simulation order for a model with one ventilation system servicing two building spaces.</p> Full article ">Figure 3
<p>Relations between measured flow rate and power consumption (<b>left</b>), and flow rate and specific power consumption (<b>right</b>) for the supply fan in “ventilation system 1”. The measurements span all of 2022 with a 1 min resolution (525,600 data points) so a low opacity is used for each point in the graphs.</p> Full article ">Figure 4
<p>Flow vs. specific power consumption for the supply fan in “ventilation system 1”. The data points are separated in three color groups according to graph coordinates. Color groups 1 and 2 indicate two clearly separated “belts” following slightly different flow rate and specific power correlations. Color group 3 contains the rest of the data points.</p> Full article ">Figure 5
<p>Selected data points of measured flow rate against measured power consumption alongside fitted fan power function for the supply fan in “ventilation system 1” (<b>left</b>) and the exhaust fan in “ventilation system 1” (<b>right</b>).</p> Full article ">Figure 6
<p>Simulated and measured values for CO<sub>2</sub> concentration (<b>left</b>) and ventilation damper position (<b>right</b>) in the building space “Ø22-511-2” (139 m<sup>2</sup> teaching room) during a workweek in 2018.</p> Full article ">Figure 7
<p>Relative weightings of each objective in the multi-objective optimization performed in the four simulated scenarios using MPC.</p> Full article ">Figure 8
<p>Electricity prices and CO<sub>2</sub> emission factors for the one-week simulation period used in MPC simulation.</p> Full article ">Figure 9
<p>Operation of Ø22-511-2 (139 m<sup>2</sup> teaching space) with the “Balanced” MPC controller for one week (Wednesday–Tuesday).</p> Full article ">Figure 10
<p>Electricity consumption, cost and CO<sub>2</sub> emission from electricity consumption for VEN1 during one week with rule-base control. Note that cost and CO<sub>2</sub> emission are measured per step (600 s).</p> Full article ">Figure 11
<p>KPI (indoor air pollution), electricity cost and CO<sub>2</sub> emission from electricity consumption for the case study building (four systems, 73 spaces) with rule-based control (Baseline) and each of the four tested MPC strategies for a one-week simulation period.</p> Full article ">

Review

Jump to: Research

17 pages, 847 KiB  
Review
A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
by Ingrida Lavrinovica, Janis Judvaitis, Dans Laksis, Marija Skromule and Kaspars Ozols
Appl. Sci. 2024, 14(21), 10057; https://doi.org/10.3390/app142110057 - 4 Nov 2024
Viewed by 3208
Abstract
In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building [...] Read more.
In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building monitoring system, this research paper explores and compares various solutions for measuring building parameters by identifying a broad spectrum of review articles considering building occupant behavior, sensor deployment, and implementation complexity. The objective of our paper is to compile diverse information on various sensors used for monitoring building conditions and provide a comprehensive overview of data structuring and processing, all within a single article. Additionally, this paper addresses the challenges of combining data from decentralized systems and the need for managerial tools to optimize user experiences. The findings contribute to the advancement of smart building management, offering valuable insights for improving building performance and user experience as well as evaluating future research directions in this field. This review is designed to serve as an introduction for anyone venturing into the field of building monitoring. Full article
(This article belongs to the Special Issue Digital Twin and IoT)
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Figure 1

Figure 1
<p>Three-layer BACS architecture [<a href="#B10-applsci-14-10057" class="html-bibr">10</a>].</p> Full article ">Figure 2
<p>Stages of NILM implementation.</p> Full article ">Figure 3
<p>Occupancy resolution levels by Melfi et al. [<a href="#B45-applsci-14-10057" class="html-bibr">45</a>].</p> Full article ">Figure 4
<p>Sensor fusion framework.</p> Full article ">Figure 5
<p>Data fusion techniques [<a href="#B59-applsci-14-10057" class="html-bibr">59</a>].</p> Full article ">
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