Quantum machine learning for energy-efficient 5G-enabled IoMT healthcare systems: enhancing data security and processing

Tools

Riaz, MZ, Behera, BK, Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149, Al-Kuwari, S and Farouk, A, 2025. Quantum machine learning for energy-efficient 5G-enabled IoMT healthcare systems: enhancing data security and processing. IEEE Internet of Things Journal. ISSN 2327-4662

Preview

Text
2478113_Mumtaz.pdf - Post-print
Download (1MB) | Preview

Official URL: https://doi.org/10.1109/jiot.2025.3586090

Abstract

Energy-efficient healthcare systems are becoming increasingly critical for Industry 5.0 as the Internet of Medical Things (IoMT) expands, particularly with the integration of 5G technology. 5G-enabled IoMT systems allow real-time data collection, high-speed communication, and enhanced connectivity between medical devices and healthcare providers. However, these systems face energy consumption and data security challenges, especially with the growing number of connected devices operating in Industry 5.0 environments with limited power resources. Quantum computing integrated with machine learning (ML) algorithms, forming quantum machine learning (QML), offers exponential improvements in computational speed and efficiency through principles such as superposition and entanglement. In this paper, we propose and evaluate three QML algorithms, which are UU, variational UU, and UU-quantum neural networks (QNN) for classifying data from four different datasets: 5G-South Asia, Lumos5G 1.0, WUSTL EHMS 2020, and PS-IoT. Our comparative analysis, using various evaluation metrics, reveals that the UU-QNN method not only outperforms the other algorithms in the 5G-South Asia and WUSTL EHMS 2020 datasets, achieving 100% accuracy, but also aligns with the human-centric goals of Industry 5.0 by allowing more efficient and secure healthcare data processing. Furthermore, the robustness of the proposed quantum algorithms is verified against several noisy channels by analyzing accuracy variations in response to each noise model parameter, which contributes to the resilience aspect of Industry 5.0. These results offer promising quantum solutions for 5G-enabled IoMT healthcare systems by optimizing data classification and reducing power consumption while maintaining high levels of security even in noisy environments.

Item Type:	Journal article
Publication Title:	IEEE Internet of Things Journal
Creators:	Riaz, M.Z., Behera, B.K., Mumtaz, S., Al-Kuwari, S. and Farouk, A.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	2025
ISSN:	2327-4662
Identifiers:	Number Type 10.1109/jiot.2025.3586090 DOI 2478113 Other
Rights:	© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	30 Jul 2025 09:05
Last Modified:	30 Jul 2025 09:05
URI:	https://irep.ntu.ac.uk/id/eprint/54062