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Future Internet
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Internet of Things (IoT) for Smart Living and Public Health,...
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Internet of Things (IoT) for Smart Living and Public Health, Volume II

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

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2047

Special Issue Editor

Prof. Dr. Hamid Mcheick

E-Mail Website
Guest Editor
Department of Computer Science and Mathematics, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B, Canada
Interests: healthcare systems; ubiquitous and pervasive computing; context-aware applications; Internet of Things (IoT); Cloud computing, smart healthcare systems; smart city
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.

Keywords

  • smart living
  • IoMT
  • smart healthcare systems

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

Published Papers (2 papers)

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Research

15 pages, 3559 KiB  
Article
Advanced Denoising and Meta-Learning Techniques for Enhancing Smart Health Monitoring Using Wearable Sensors
by Minyechil Alehegn Tefera, Amare Mulatie Dehnaw, Yibeltal Chanie Manie, Cheng-Kai Yao, Shegaw Demessie Bogale and Peng-Chun Peng
Future Internet 2024, 16(8), 280; https://doi.org/10.3390/fi16080280 - 5 Aug 2024
Viewed by 871
Abstract
This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the [...] Read more.
This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems. Full article
(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health, Volume II)
►▼ Show Figures

Figure 1

Figure 1
<p>Proposed discrete wavelet transform (DWT) multi-level denoising (<b>a</b>) before applying the denoising technique, (<b>b</b>) after applying the denoising technique.</p> Full article ">Figure 2
<p>The scheme and implementation framework of the proposed system.</p> Full article ">Figure 3
<p>Proposed technique performance based on RMSE and MSE according to various iterations (k) steps.</p> Full article ">Figure 4
<p>Comparison of performance of meta-learning, without pre-train, after fine-tune, and before fine-tune in terms of MAE and time.</p> Full article ">Figure 5
<p>Predicted vs actual glucose level using the proposed method.</p> Full article ">Figure 6
<p>ROC curve of the proposed model.</p> Full article ">Figure 7
<p>Detected r-peaks using the proposed method.</p> Full article ">Figure 8
<p>Performance of different models.</p> Full article ">
21 pages, 4618 KiB  
Article
Towards an Optimized Blockchain-Based Secure Medical Prescription-Management System
by Imen Ahmed, Mariem Turki, Mouna Baklouti, Bouthaina Dammak and Amnah Alshahrani
Future Internet 2024, 16(7), 243; https://doi.org/10.3390/fi16070243 - 9 Jul 2024
Viewed by 747
Abstract
This work introduces a blockchain-based secure medical prescription-management system seamlessly integrated with a dynamic Internet of Things (IoT) framework. Notably, this integration constitutes a pivotal challenge in the arena of resource-constrained IoT devices: energy consumption. The choice of a suitable blockchain consensus mechanism [...] Read more.
This work introduces a blockchain-based secure medical prescription-management system seamlessly integrated with a dynamic Internet of Things (IoT) framework. Notably, this integration constitutes a pivotal challenge in the arena of resource-constrained IoT devices: energy consumption. The choice of a suitable blockchain consensus mechanism emerges as the linchpin in surmounting this hurdle. Thus, this paper conducts a comprehensive comparison of energy consumption between two distinct consensus mechanisms: Proof of Work (PoW) and Quorum-based Byzantine fault tolerance (QBFT). Furthermore, an assessment of the most energy-efficient algorithm is performed across multiple networks and various parameters. This approach ensures the acquisition of reliable and statistically significant data, enabling meaningful conclusions to be drawn about the system’s performance in real-world scenarios. The experimental results show that, compared to the PoW, the QBFT consensus mechanism reduced the energy consumption by an average of 5%. This finding underscores the significant advantage of QBFT in addressing the energy consumption challenges posed by resource-constrained IoT devices. In addition to its inherent benefits of privacy and block time efficiency, the Quorum blockchain emerges as a more sustainable choice for IoT applications due to its lower power consumption. Full article
(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health, Volume II)
►▼ Show Figures

Figure 1

Figure 1
<p>Blockchain-based prescription-management architecture.</p> Full article ">Figure 2
<p>Sequence diagram of the proposed system.</p> Full article ">Figure 3
<p>Implementation environment.</p> Full article ">Figure 4
<p>User authentication and add prescription interfaces.</p> Full article ">Figure 5
<p>IoT system composed of Raspberry Pi and Pi camera.</p> Full article ">Figure 6
<p>Implementation of three-node blockchain network.</p> Full article ">Figure 7
<p>Energy consumption measurement using USB voltage- and current-detection module.</p> Full article ">Figure 8
<p>Energy consumption (measured in watts) comparison between Ethereum and Quorum.</p> Full article ">Figure 9
<p>Energy consumption (measured in watts) according to the number of function parameters in Quorum blockchain.</p> Full article ">Figure 10
<p>Effect of send rate on throughput.</p> Full article ">Figure 11
<p>Effect of send rate on latency.</p> Full article ">Figure 12
<p>Throughput and latency of the function getPresc.</p> Full article ">
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