Meta-verse assisted healthcare body sensor network architecture

Mastoi, Qurat-Ul-Ain; Lakhan, Abdullah

Abstract

These days, the usage of body area network enabled healthcare services has been increasing widely in practice. The objective is to monitor healthcare in real time from different remote services. However, the existing body area network architecture for healthcare services is not sufficient and optimal. In this paper, we present a Meta- Verse-based Healthcare Body Sensor Network Architecture to offer seamless healthcare services to different subjects. The architecture consisted of metaverse technology, healthcare sensors, and fog nodes to offer seamless services to the subjects. In architecture, we consider the hetero-geneous devices such as medical sensors (e.g., heart rate, oxygen, blood pressure, and temperature) to be connected with the mobile devices and metaverse fog nodes to process the healthcare application tasks of subjects. This paper presents the meta verse body area network offloading scheduling (MBANOS) based on a convolutional neural network (CNN) for all healthcare sensor data and their tasks. Simulation results show that MBANOS outperformed and obtained the highest score of data processing on different nodes to maintain the subject healthcare in practice.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE 20th International Conference on Body Sensor Networks (BSN)
• Start Date: Oct 15, 2024
• End Date: Oct 17, 2024
• Acceptance Date: Jul 29, 2024
• Online Publication Date: Dec 11, 2024
• Publication Date: Dec 11, 2024
• Deposit Date: Jan 25, 2025
• Publicly Available Date: Feb 26, 2025
• Journal: 2024 IEEE 20th International Conference on Body Sensor Networks (BSN)
• Print ISSN: 1557-170X
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Book Title: 2024 IEEE 20th International Conference on Body Sensor Networks (BSN)
• ISBN: 9798331530150
• DOI: https://doi.org/10.1109/bsn63547.2024.10780550
• Public URL: https://uwe-repository.worktribe.com/output/13549723