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Machine Learning for IoT Applications and Digital Twins II

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 29158

Special Issue Editors

Crown Institute of Higher Education (CIHE), Sydney, Australia
Interests: internet of things (IOT); localization; machine learning
Special Issues, Collections and Topics in MDPI journals
Victoria University Business School, Melbourne, Victoria, Australia
Interests: Internet of Things (IoT); Machine Learning; data analytics
Special Issues, Collections and Topics in MDPI journals
Institut Polytechnique de Paris, Telecom SudParis, CNRS Lab, Evry, France
Interests: Internet of Things (IoT); data science; social networks
Special Issues, Collections and Topics in MDPI journals
Crown Institute of Higher Education (CIHE), Sydney, Australia
Interests: cybersecurity; cloud computing; IoT
* Associate Professor

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT), one of the emergent technologies that has improved the living environment of human beings, is the source of Big Data generation. In IoT networks, there are many ubiquitous interconnected sensors from different machines or devices. There is a necessity of having novel tools and techniques for processing the huge volume of data and transform them to knowledge. In addition, machine learning techniques have been used comprehensively for a variety of IoT applications. Analysis of IoT sensor data with machine learning algorithms is key for achieving useful information for prediction, classification, data association. and data conceptualization.

On the other hand, Digital Twin integrates IoT, Artificial Intelligence, and Machine Learning with Software Analytics to create digital living.

Thus, this Special Issue welcomes original contributions and review papers on Machine Learning for IoT applications and Digital Twin, in the following potential areas:

  • Machine Learning for Smart City/Smart Home/Smart Transportation;
  • Machine Learning for Smart Health/Smart Wearable Devices;
  • Machine Learning for Smart Industry/Smart Grid/Smart Agriculture;
  • Digital Twins integrated with IoT;
  • Smart Applications of Digital Twin;
  • Data-driven scenarios based on Digital Twin leveraging AI;
  • Blockchain and Security for Digital Twin.

Dr. Javad Rezazadeh
Dr. Omid Ameri Sianaki
Dr. Reza Farahbakhsh
Dr. John Ayoade
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.

Published Papers (9 papers)

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Research

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30 pages, 8051 KiB  
Article
Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study
by Alberto Armijo and Diego Zamora-Sánchez
Sensors 2024, 24(7), 2115; https://doi.org/10.3390/s24072115 - 26 Mar 2024
Viewed by 209
Abstract
Structural health monitoring (SHM) is critical for ensuring the safety of infrastructure such as bridges. This article presents a digital twin solution for the SHM of railway bridges using low-cost wireless accelerometers and machine learning (ML). The system architecture combines on-premises edge computing [...] Read more.
Structural health monitoring (SHM) is critical for ensuring the safety of infrastructure such as bridges. This article presents a digital twin solution for the SHM of railway bridges using low-cost wireless accelerometers and machine learning (ML). The system architecture combines on-premises edge computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, the accelerometers stream raw vibration data, which are processed in the frequency domain and analyzed using machine learning to detect anomalies that indicate potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge for which vibration data were collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks that indicate potential changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warnings of bridge damage and also supports the use of in-house-designed sensors with lower cost and edge computing capabilities such as those used in the demonstration. The successful on-premises–cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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23 pages, 5004 KiB  
Article
Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach
by Muhammed Zahid Karakusak, Hasan Kivrak, Simon Watson and Mehmet Kemal Ozdemir
Sensors 2023, 23(24), 9903; https://doi.org/10.3390/s23249903 - 18 Dec 2023
Viewed by 1061
Abstract
In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio [...] Read more.
In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of 2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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22 pages, 2942 KiB  
Article
Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion
by Izaldein Al-Zyoud, Fedwa Laamarti, Xiaocong Ma, Diana Tobón and Abdulmotaleb El Saddik
Sensors 2022, 22(24), 9747; https://doi.org/10.3390/s22249747 - 12 Dec 2022
Cited by 10 | Viewed by 3068
Abstract
Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three [...] Read more.
Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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19 pages, 6371 KiB  
Article
Augmented Analytics Driven by AI: A Digital Transformation beyond Business Intelligence
by Noorah A. Alghamdi and Heyam H. Al-Baity
Sensors 2022, 22(20), 8071; https://doi.org/10.3390/s22208071 - 21 Oct 2022
Cited by 5 | Viewed by 5038
Abstract
Lately, Augmented Analytics (AA) has increasingly been introduced as a tool for transforming data into valuable insights for decision-making, and it has gained attention as one of the most advanced methods to facilitate modern analytics for different types of users. AA can be [...] Read more.
Lately, Augmented Analytics (AA) has increasingly been introduced as a tool for transforming data into valuable insights for decision-making, and it has gained attention as one of the most advanced methods to facilitate modern analytics for different types of users. AA can be defined as a combination of Business Intelligence (BI) and the advanced features of Artificial Intelligence (AI). With the massive growth in data diversity, the traditional approach to BI has become less useful and requires additional work to obtain timely results. However, the power of AA that uses AI can be leveraged in BI platforms with the use of Machine Learning (ML) and natural language comprehension to automate the cycle of business analytics. Despite the various benefits for businesses and end users in converting from BI to AA, research on this trend has been limited. This study presents a comparison of the capabilities of the traditional BI and its augmented version in the business analytics cycle. Our findings show that AA enhances analysis, reduces time, and supports data preparation, visualization, modelling, and generation of insights. However, AI-driven analytics cannot fully replace human decision-making, as most business problems cannot be solved purely by machines. Human interaction and perspectives are essential, and decision-makers still play an important role in sharing and operationalizing findings. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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16 pages, 3643 KiB  
Article
Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning
by Muhammad Umar Nasir, Muhammad Zubair, Taher M. Ghazal, Muhammad Farhan Khan, Munir Ahmad, Atta-ur Rahman, Hussam Al Hamadi, Muhammad Adnan Khan and Wathiq Mansoor
Sensors 2022, 22(19), 7483; https://doi.org/10.3390/s22197483 - 02 Oct 2022
Cited by 15 | Viewed by 2328
Abstract
Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start [...] Read more.
Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient’s data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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31 pages, 13532 KiB  
Article
Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS
by Junsup Song, Dimitris Karagiannis and Moonkun Lee
Sensors 2022, 22(13), 5057; https://doi.org/10.3390/s22135057 - 05 Jul 2022
Cited by 3 | Viewed by 1743
Abstract
This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, [...] Read more.
This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, since each IoT can be represented as a process; a lattice can also be considered one of the best mathematical structures to abstract the collective behavior of IoTs since it has the hierarchical structure to represent multi-dimensional aspects of the interactions of IoTs. The dual approach using two mathematical structures is very challenging since the process algebra have to provide an expressive power to describe the smart behavior of IoTs, and the lattice has to provide an operational capability to handle the state-explosion problem generated from the interactions of IoTs. For these purposes, this paper presents a process algebra, called dTP-Calculus, which represents the smart behavior of IoTs with non-deterministic choice operation based on probability, and a lattice, called n:2-Lattice, which has special join and meet operations to handle the state explosion problem. The main advantage of the method is that the lattice can represent all the possible behavior of the IoT systems, and the patterns of behavior can be elaborated by finding the traces of the behavior in the lattice. Another main advantage is that the new notion of equivalences can be defined within n:2-Lattice, which can be used to solve the classical problem of exponential and non-deterministic complexity in the equivalences of Norm Chomsky and Robin Milner by abstracting them into polynomial and static complexity in the lattice. In order to prove the concept of the method, two tools are developed based on the ADOxx Meta-Modeling Platform: SAVE for the dTP-Calculus and PRISM for the n:2-Lattice. The method and tools can be considered one of the most challenging research topics in the area of modeling to represent the collective behavior of Smart IoT Systems. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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23 pages, 5724 KiB  
Article
Integration of Digital Twin, Machine-Learning and Industry 4.0 Tools for Anomaly Detection: An Application to a Food Plant
by Giovanni Paolo Tancredi, Giuseppe Vignali and Eleonora Bottani
Sensors 2022, 22(11), 4143; https://doi.org/10.3390/s22114143 - 30 May 2022
Cited by 11 | Viewed by 2562
Abstract
This work describes a structured solution that integrates digital twin models, machine-learning algorithms, and Industry 4.0 technologies (Internet of Things in particular) with the ultimate aim of detecting the presence of anomalies in the functioning of industrial systems. The proposed solution has been [...] Read more.
This work describes a structured solution that integrates digital twin models, machine-learning algorithms, and Industry 4.0 technologies (Internet of Things in particular) with the ultimate aim of detecting the presence of anomalies in the functioning of industrial systems. The proposed solution has been designed to be suitable for implementation in industrial plants not directly designed for Industry 4.0 applications. More precisely, this manuscript delineates an approach for implementing three machine-learning algorithms into a digital twin environment and then applying them to a real plant. This paper is based on two previous studies in which the digital twin environment was first developed for the industrial plant under investigation, and then used for monitoring selected plant parameters. Findings from the previous studies are exploited in this work and advanced by implementing and testing the machine-learning algorithms. The results show that two out of the three machine-learning algorithms are effective enough in predicting anomalies, thus suggesting their implementation for enhancing the safety of employees working at industrial plants. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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13 pages, 1885 KiB  
Article
Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis
by Hsin Hsiu, Shun-Ku Lin, Wan-Ling Weng, Chaw-Mew Hung, Che-Kai Chang, Chia-Chien Lee and Chao-Tsung Chen
Sensors 2022, 22(3), 806; https://doi.org/10.3390/s22030806 - 21 Jan 2022
Cited by 9 | Viewed by 1534
Abstract
Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of [...] Read more.
Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of the arterial pulse waveform can be used to discriminate different cognitive conditions of community subjects. 3-min Radial arterial blood pressure waveform (BPW) signals were measured noninvasively in 123 subjects. Eight machine-learning algorithms were used to evaluate the following 4 pulse indices for 10 harmonics (total 40 BPW spectral indices): amplitude proportion and its coefficient of variation; phase angle and its standard deviation. Significant differences were noted in the spectral pulse indices between Alzheimer’s-disease patients and control subjects. Using them as training data (AUC = 70.32% by threefold cross-validation), a significant correlation (R2 = 0.36) was found between the prediction probability of the test data (comprising community subjects at two sites) and the Mini-Mental-State-Examination score. This finding illustrates possible physiological connection between arterial pulse transmission and cognitive function. The present findings from pulse-wave and machine-learning analyses may be useful for discriminating cognitive condition, and hence in the development of a user-friendly, noninvasive, and rapid method for the early screening of dementia. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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Review

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27 pages, 736 KiB  
Review
Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging
by Mahsa Arabahmadi, Reza Farahbakhsh and Javad Rezazadeh
Sensors 2022, 22(5), 1960; https://doi.org/10.3390/s22051960 - 02 Mar 2022
Cited by 69 | Viewed by 9677
Abstract
Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and [...] Read more.
Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on “braintumor” website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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