Internet of Things (IoT) for Smart Living and Public Health

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (17 October 2023) | Viewed by 27991

Special Issue Editor


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Guest Editor
Department of Computer Science and Mathematics, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B, Canada
Interests: design context-aware smart and adaptive systems; design healthcare systems; design emerging architectures (IoT); software adaptation; smart living
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of The Internet of Things (IoT) has evolved due to the convergence of multiple technologies, including ubiquitous computing, commodity sensors, increasingly powerful embedded systems, and machine learning. Traditional fields of embedded systems, wireless sensor networks, control systems, and automation (including home and building automation), independently and collectively, enable the Internet of Things. In the consumer market, IoT technology is most synonymous with products pertaining to the concept of “smart living”, including devices and appliances (such as lighting fixtures, thermostats, home security systems, cameras, and other home appliances) that support one or more common ecosystems and can be controlled via devices associated with that ecosystem, such as smartphones and smart speakers. The IoT is also used in healthcare systems.

Hence, we are inviting innovative solutions that highlight relevant Internet of Things issues, challenges, and solutions for smart living and Public Health. Topics include, but are not limited to:

  • Smart IoT technologies;
  • Impact of IoT technologies on smart living;
  • Testbeds, applications, case studies, and social issues in the IoT environment;
  • Smart healthcare systems;
  • Privacy and security in healthcare;
  • Machine learning algorithms for medical diagnosis;
  • Electronic wearable solutions for smart homes and workplaces;
  • IoT-enabled healthcare services;
  • Embedded systems in healthcare;
  • AI-based healthcare diagnosis and prevention;
  • Latest trends and technologies in the IoMT and AIoMT area;
  • Fog- and edge-computing-based healthcare framework.

Prof. Dr. Hamid Mcheick
Guest Editor

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. Future Internet 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 1600 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 (6 papers)

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Research

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21 pages, 1310 KiB  
Article
Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
by Alireza Tavakolian, Alireza Rezaee, Farshid Hajati and Shahadat Uddin
Future Internet 2023, 15(9), 304; https://doi.org/10.3390/fi15090304 - 06 Sep 2023
Viewed by 1520
Abstract
Hospital readmission and length-of-stay predictions provide information on how to manage hospital bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called the Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN), with a unique preprocessing method to [...] Read more.
Hospital readmission and length-of-stay predictions provide information on how to manage hospital bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called the Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN), with a unique preprocessing method to predict hospital readmission and the length of stay required for patients of various conditions. GAOCNN uses one-dimensional convolutional layers to predict hospital readmission and the length of stay. The parameters of the layers are optimized via a genetic algorithm. To show the performance of the proposed model in patients with various conditions, we evaluate the model under three healthcare datasets: the Diabetes 130-US hospitals dataset, the COVID-19 dataset, and the MIMIC-III dataset. The diabetes 130-US hospitals dataset has information on both readmission and the length of stay, while the COVID-19 and MIMIC-III datasets just include information on the length of stay. Experimental results show that the proposed model’s accuracy for hospital readmission was 97.2% for diabetic patients. Furthermore, the accuracy of the length-of-stay prediction was 89%, 99.4%, and 94.1% for the diabetic, COVID-19, and ICU patients, respectively. These results confirm the superiority of the proposed model compared to existing methods. Our findings offer a platform for managing the healthcare funds and resources for patients with various diseases. Full article
(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health)
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19 pages, 12249 KiB  
Article
Internet of Robotic Things (IoRT) and Metaheuristic Optimization Techniques Applied for Wheel-Legged Robot
by Mateusz Malarczyk, Grzegorz Kaczmarczyk, Jaroslaw Szrek and Marcin Kaminski
Future Internet 2023, 15(9), 303; https://doi.org/10.3390/fi15090303 - 06 Sep 2023
Viewed by 1107
Abstract
This paper presents the operation of a remotely controlled, wheel-legged robot. The developed Wi-Fi connection framework is established on a popular ARM microcontroller board. The implementation provides a low-cost solution that is in congruence with the newest industrial standards. Additionally, the problem of [...] Read more.
This paper presents the operation of a remotely controlled, wheel-legged robot. The developed Wi-Fi connection framework is established on a popular ARM microcontroller board. The implementation provides a low-cost solution that is in congruence with the newest industrial standards. Additionally, the problem of limb structure and motor speed control is solved. The design process of the mechanical structure is enhanced by a nature-inspired metaheuristic optimization algorithm. An FOC-based BLDC motor speed control strategy is selected to guarantee dynamic operation of the drive. The paper provides both the theoretical considerations and the obtained prototype experimental results. Full article
(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health)
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25 pages, 2214 KiB  
Article
Clinical Screening Prediction in the Portuguese National Health Service: Data Analysis, Machine Learning Models, Explainability and Meta-Evaluation
by Teresa Gonçalves, Rute Veladas, Hua Yang, Renata Vieira, Paulo Quaresma, Paulo Infante, Cátia Sousa Pinto, João Oliveira, Maria Cortes Ferreira, Jéssica Morais, Ana Raquel Pereira, Nuno Fernandes and Carolina Gonçalves
Future Internet 2023, 15(1), 26; https://doi.org/10.3390/fi15010026 - 03 Jan 2023
Viewed by 1727
Abstract
This paper presents an analysis of the calls made to the Portuguese National Health Contact Center (SNS24) during a three years period. The final goal was to develop a system to help nurse attendants select the appropriate clinical pathway (from 59 options) for [...] Read more.
This paper presents an analysis of the calls made to the Portuguese National Health Contact Center (SNS24) during a three years period. The final goal was to develop a system to help nurse attendants select the appropriate clinical pathway (from 59 options) for each call. It examines several aspects of the calls distribution like age and gender of the user, date and time of the call and final referral, among others and presents comparative results for alternative classification models (SVM and CNN) and different data samples (three months, one and two years data models). For the task of selecting the appropriate pathway, the models, learned on the basis of the available data, achieved F1 values that range between 0.642 (3 months CNN model) and 0.783 (2 years CNN model), with SVM having a more stable performance (between 0.743 and 0.768 for the corresponding data samples). These results are discussed regarding error analysis and possibilities for explaining the system decisions. A final meta evaluation, based on a clinical expert overview, compares the different choices: the nurse attendants (reference ground truth), the expert and the automatic decisions (2 models), revealing a higher agreement between the ML models, followed by their agreement with the clinical expert, and minor agreement with the reference. Full article
(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health)
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16 pages, 901 KiB  
Article
SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems
by Muhammad Asad, Muhammad Aslam, Syeda Fizzah Jilani, Saima Shaukat and Manabu Tsukada
Future Internet 2022, 14(11), 338; https://doi.org/10.3390/fi14110338 - 18 Nov 2022
Cited by 6 | Viewed by 1771
Abstract
Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications provide access to the clients’ health information. [...] Read more.
Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications provide access to the clients’ health information. However, the rapid increase in the number of mobile devices and social networks has generated concerns regarding the secure sharing of a client’s location. In this regard, federated learning (FL) is an emerging paradigm of decentralized machine learning that guarantees the training of a shared global model without compromising the data privacy of the client. To this end, we propose a K-anonymity-based secure hierarchical federated learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates hierarchically with multiple directly and indirectly connected devices. In particular, the proposed SHFL formulates the hierarchical clusters of location-based services to achieve distributed FL. In addition, the proposed SHFL utilizes the K-anonymity method to hide the location of the cluster devices. Finally, we evaluated the performance of the proposed SHFL by configuring different hierarchical networks with multiple model architectures and datasets. The experiments validated that the proposed SHFL provides adequate generalization to enable network scalability of accurate healthcare systems without compromising the data and location privacy. Full article
(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health)
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Review

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34 pages, 2309 KiB  
Review
Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions
by Elarbi Badidi
Future Internet 2023, 15(11), 370; https://doi.org/10.3390/fi15110370 - 18 Nov 2023
Cited by 3 | Viewed by 3497
Abstract
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed [...] Read more.
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health. Full article
(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health)
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36 pages, 5618 KiB  
Review
Quantum Computing for Healthcare: A Review
by Raihan Ur Rasool, Hafiz Farooq Ahmad, Wajid Rafique, Adnan Qayyum, Junaid Qadir and Zahid Anwar
Future Internet 2023, 15(3), 94; https://doi.org/10.3390/fi15030094 - 27 Feb 2023
Cited by 20 | Viewed by 17279
Abstract
In recent years, the interdisciplinary field of quantum computing has rapidly developed and garnered substantial interest from both academia and industry due to its ability to process information in fundamentally different ways, leading to hitherto unattainable computational capabilities. However, despite its potential, the [...] Read more.
In recent years, the interdisciplinary field of quantum computing has rapidly developed and garnered substantial interest from both academia and industry due to its ability to process information in fundamentally different ways, leading to hitherto unattainable computational capabilities. However, despite its potential, the full extent of quantum computing’s impact on healthcare remains largely unexplored. This survey paper presents the first systematic analysis of the various capabilities of quantum computing in enhancing healthcare systems, with a focus on its potential to revolutionize compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization. Through a comprehensive analysis of existing literature, we have developed taxonomies across different dimensions, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, providing a panoramic view of the quantum computing paradigm for healthcare. Our survey aims to aid both new and experienced researchers in quantum computing and healthcare by helping them understand the current research landscape, identifying potential opportunities and challenges, and making informed decisions when designing new architectures and applications for quantum computing in healthcare. Full article
(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health)
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