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Advances in IoMT for Healthcare Systems–2nd Edition
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Advances in IoMT for Healthcare Systems–2nd Edition

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

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 3281

Special Issue Editors

Prof. Dr. Jin-Ghoo Choi

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: wireless sensor networks; internet-of-things; mobile and wireless networks
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Shafiq

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: Internet of Things; vehicle-to-everything communication; smart cities; machine learning, computational intelligence; data science; human factors engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of micro-computing devices such as sensors, actuators, RFID tags, and machine-to-machine (M2M) communications has enabled new Internet of Things (IoT) solutions to reshape many network applications. One example of this is the healthcare system, which has been revolutionized by IoT solutions due to the introduction of the Internet of Medical Things (IoMT) system. The IoMT solution has attracted a great deal of attention because of its ability to autonomously acquire, analyze, and share data on the Internet with the help of micro-computing medical devices, data-processing algorithms, and supporting communication protocols. Modern healthcare services widely use IoMT applications to remotely monitor patients with chronic diseases and improve their quality of life. However, the adoption of IoMT faces many challenges, such as the interoperability between medical systems, the design of wearable (and/or ambient) medical devices, medical data analysis, the security and privacy of patient records, and the interaction between patients and medical devices. In this regard, modern computing technologies (such as machine learning, cloud/edge computing, and data mining), soft computing methods, and dedicated communication protocols could help provide services for the IoMT system to improve timely patient diagnosis. It could also improve disease control, treatment methods, drug management, and improve the user experience of the patient and medical staff.

The purpose of this Special Issue is to provide the latest reference material on the theoretical and practical challenges, as well as innovative ideas and solutions for the IoMT in healthcare systems. The scope of this Special Issue includes (but is not limited to) the following: high-performance and resilient infrastructure for IoMT systems; ontology-based recommendations and disease identification systems; innovative wearable and/or ambient IoMT devices; the collection, modeling, and evaluation of big data for IoMT systems; in-hospital and in-home healthcare functions for IoMT systems; M2M interoperability and communication protocols for IoMT systems; optimized data security, privacy, and trust for IoMT systems; AI-based IoMT in telehealth virtual consulting and patient monitoring; innovative human–computer interaction models for IoMT systems; legal, ethical, and social considerations in IoMT for healthcare systems; AI-based signal and image processing applied to health; data analysis for health issues; EEG signals and systems.

Prof. Dr. Jin-Ghoo Choi
Dr. Muhammad Shafiq
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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Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 567 KiB  
Article
ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System
by Babu Kaji Baniya
Sensors 2024, 24(20), 6601; https://doi.org/10.3390/s24206601 - 13 Oct 2024
Viewed by 1240
Abstract
The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it [...] Read more.
The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems–2nd Edition)
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18 pages, 1131 KiB  
Article
Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems
by Mousa Alalhareth and Sung-Chul Hong
Sensors 2024, 24(11), 3519; https://doi.org/10.3390/s24113519 - 30 May 2024
Cited by 1 | Viewed by 1350
Abstract
This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security [...] Read more.
This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model’s superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems–2nd Edition)
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