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Special Issue "Effect of 6G and beyond Communication Technologies on Healthcare Sector"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 November 2023 | Viewed by 840

Special Issue Editors

Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11451, Saudi Arabia
Interests: information security; wsn; m2m; manets; mobile computing, iot; wmns; intelligent autonomous systems; biological inspired optimization algorithms
Post-Graduation Program in Electrical Engineering, Federal University of Piauí (UFPI), Teresina-Pi 64049-550, Brazil
Interests: Internet of Things; wireless and body sensor networks; mobile and e-health technologies; future internet technologies; vehicular communications; mobile and cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital transformation has dramatically reshaped the healthcare industry by providing access to modern diagnostic tools, health analytics and advancements in minimally invasive robotic surgery. Digital healthcare has facilitated the use of telemedicine, online consultations, remote monitoring and many more services. The use of the Internet-of-Things (IoT) in the healthcare sector has fueled the current digital healthcare system by collecting patients’ data. With the utilization of more and more wearable devices, 5G will need to shift to 6G for enhanced communication. Instead of the smart technology era, 6G is expected to be a truly AI-driven technology era, where IoT will be switched to the Internet of Everything. When enabling 6G technologies, there are accompanying issues and challenges that need to be analyzed.

This Special Issue focuses on a wide range of issues related to the application of 6G in various fields. The main focus of this Special Issue is digital healthcare.

Topics to be covered include, but are not limited to, the following:

  • Detection of EEG, EMG and ECG using smart wearable devices and AI
  • Big data analytics and deep learning in 6G
  • Data privacy and security in future healthcare
  • Data storage, collection, processing, training and modeling in 6G
  • Federated learning for healthcare in 6G
  • Advanced AI algorithms for the automatic examination of physiological signals obtained from wearable devices

Dr. Kashif Saleem
Prof. Dr. Joel J. P. C. Rodrigues
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Sustainability 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 2200 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.


  • internet of things
  • healthcare sector
  • 6G technologies
  • artificial intelligence
  • digital healthcare

Published Papers (1 paper)

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PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection
Sustainability 2023, 15(5), 4094; - 23 Feb 2023
Viewed by 527
Diagnosing Autism spectrum disorder (ASD) is a challenging task for clinicians due to the inconsistencies in existing medical tests. The Internet of things (IoT) has been used in several medical applications to realize advancements in the healthcare industry. Using machine learning in tandem [...] Read more.
Diagnosing Autism spectrum disorder (ASD) is a challenging task for clinicians due to the inconsistencies in existing medical tests. The Internet of things (IoT) has been used in several medical applications to realize advancements in the healthcare industry. Using machine learning in tandem IoT can enhance the monitoring and detection of ASD. To date, most ASD studies have relied primarily on the operational connectivity and structural metrics of fMRI data processing while neglecting the temporal dynamics components. Our research proposes Progressive Fourier Transform (PFT), a novel time-frequency decomposition, together with a Convolutional Neural Network (CNN), as a preferred alternative to available ASD detection systems. We use the Autism Brain Imaging Data Exchange dataset for model validation, demonstrating better results of the proposed PFT model compared to the existing models, including an increase in accuracy to 96.7%. These results show that the proposed technique is capable of analyzing rs-fMRI data from different brain diseases of the same type. Full article
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