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Smart Healthcare in the Internet of Things: Recent Developments in a Pandemic Age

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3474

Special Issue Editors

Prof. Dr. Arnab Majumdar

E-Mail Website
Guest Editor
Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
Interests: electrocardiography; failure analysis; fault diagnosis; medical signal processing; moving average processes; public transport; rail traffic; railway accidents; railway industry; railway safety; road accidents; road safety; traffic engineering computing
Dr. Wen-Te Liu

E-Mail Website
Guest Editor
1. School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
2. Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan
3. Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan
Interests: medical signal processing; electrocardiography; patient diagnosis; analogue-digital conversion; electroencephalography; signal sampling; wireless sensor networks; biomedical electronics; biomedical measurement
Dr. Alberto Martinetti

E-Mail Website
Guest Editor
Design, Production and Management Department, University of Twente, 7522 Enschede, The Netherlands
Interests: occupational safety and health; risk analysis; hazard identification techniques; maintenance engineering and management; system reliability and human factors; antifragility engineering; application and utilization of emergent technologies to maintenance and safety topics
Special Issues, Collections and Topics in MDPI journals
Dr. Orawit Thinnukool

E-Mail Website
Guest Editor
College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: computer vision; data dissemination; disease diagnosis; epidemics; artificial intelligence; medical computing; microorganisms; mobile ad hoc networks; mobility management (mobile radio); natural language processing; routing protocols; telecommunication network reliability; telecommunication network topology

Special Issue Information

Dear Colleagues,

Remote health monitoring has played an increasingly important role, especially in light of the recent era of pandemics. Researchers have identified the feasibility of integrating deep learning, the cloud, blockchain, and the Internet of Things (IoT) to improve overall automation and use it for disease detection and diagnosis.

This special issue aims to focus on the various technologies used in smart healthcare, including: deep learning, blockchain-enabled technologies, cloud-based IoT applications, fog computing in smart healthcare, and the challenges and issues faced by smart healthcare. Applications may include disease prevention and diagnosis, health and patient monitoring,  mobile health and remote surgery.

We welcome submissions that include, but are not limited to, the following broad range of topics:

  • AI-driven methods for resource allocation for Healthcare Applications in secure Edge Computing applications
  • Energy efficient and secure Ambient Assisted Living for Remote Healthcare Applications
  • QoS/QoE monitoring in private and secure mobile healthcare applications and Resources
  • Blockchain-Enabled Antenna design methods for Body-centric medical health
  • Blockchain-Enabled Power-aware Bio-Nano and MEMS frameworks for IoMT
  • Blockchain technologies for reliable and trustworthy computing
  • Multi-Omics Enabled Secure Disease Prediction in Distributed Fog Cloud Networks
  • Biometric security for medical applications Based on Machine Learning Schemes
  • Physiological-based data Blockchain-Enabled protocols for health monitoring
  • Resource-constrained security for Mobility Enabled Mobile Applications
  • Lightweight authentication and data encryption Schemes Based on Homomorphic Techniques
  • Physical layer security for Healthcare Digital Conferencing
  • Machine Learning as a solution and risk factor for Healthcare IoT security Systems 

Prof. Dr. Arnab Majumdar
Dr. Wen-Te Liu
Dr. Alberto Martinetti
Dr. Orawit Thinnukool
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 (2 papers)

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Research

21 pages, 3231 KiB  
Article
RBEF: Ransomware Efficient Public Blockchain Framework for Digital Healthcare Application
by Abdullah Lakhan, Orawit Thinnukool, Tor Morten Groenli and Pattaraporn Khuwuthyakorn
Sensors 2023, 23(11), 5256; https://doi.org/10.3390/s23115256 - 01 Jun 2023
Cited by 1 | Viewed by 1695
Abstract
These days, the use of digital healthcare has been growing in practice. Getting remote healthcare services without going to the hospital for essential checkups and reports is easy. It is a cost-saving and time-saving process. However, digital healthcare systems are suffering from security [...] Read more.
These days, the use of digital healthcare has been growing in practice. Getting remote healthcare services without going to the hospital for essential checkups and reports is easy. It is a cost-saving and time-saving process. However, digital healthcare systems are suffering from security and cyberattacks in practice. Blockchain technology is a promising technology that can process valid and secure remote healthcare data among different clinics. However, ransomware attacks are still complex holes in blockchain technology and prevent many healthcare data transactions during the process on the network. The study presents the new ransomware blockchain efficient framework (RBEF) for digital networks, which can identify transaction ransomware attacks. The objective is to minimize transaction delays and processing costs during ransomware attack detection and processing. The RBEF is designed based on Kotlin, Android, Java, and socket programming on the remote process call. RBEF integrated the cuckoo sandbox static and dynamic analysis application programming interface (API) to handle compile-time and runtime ransomware attacks in digital healthcare networks. Therefore, code-, data-, and service-level ransomware attacks are to be detected in blockchain technology (RBEF). The simulation results show that the RBEF minimizes transaction delays between 4 and 10 min and processing costs by 10% for healthcare data compared to existing public and ransomware efficient blockchain technologies healthcare systems. Full article
(This article belongs to the Special Issue Smart Healthcare in the Internet of Things: Recent Developments in a Pandemic Age)
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29 pages, 7279 KiB  
Article
Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing
by Ali Yavari, Harindu Korala, Dimitrios Georgakopoulos, Jonathan Kua and Hamid Bagha
Sensors 2023, 23(11), 5211; https://doi.org/10.3390/s23115211 - 30 May 2023
Cited by 2 | Viewed by 1254
Abstract
The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this [...] Read more.
The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance. Full article
(This article belongs to the Special Issue Smart Healthcare in the Internet of Things: Recent Developments in a Pandemic Age)
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