Document Type

Article

Source of Publication

Scientific Reports

Publication Date

12-1-2025

Abstract

The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitive patient data. Traditional cloud-based architectures often struggle with latency and data protection, making them inefficient for real-time healthcare scenarios. To address these challenges, we propose a Hybrid Fog-Edge Computing Architecture tailored for effective real-time health monitoring in IoMT systems. Fog computing enables processing of time-critical data closer to the data source, reducing response time and relieving cloud system overload. Simultaneously, edge computing nodes handle data preprocessing and transmit only valuable information—defined as abnormal or high-risk health signals such as irregular heart rate or oxygen levels—using rule-based filtering, statistical thresholds, and lightweight machine learning models like Decision Trees and One-Class SVMs. This selective transmission optimizes bandwidth without compromising response quality. The architecture integrates robust security measures, including end-to-end encryption and distributed authentication, to counter rising data breaches and unauthorized access in IoMT networks. Real-life case scenarios and simulations are used to validate the model, evaluating latency reduction, data consolidation, and scalability. Results demonstrate that the proposed architecture significantly outperforms cloud-only models, with a 70% latency reduction, 30% improvement in energy efficiency, and 60% bandwidth savings. Additionally, the time required for threat detection was halved, ensuring faster response to security incidents. This framework offers a flexible, secure, and efficient solution ideal for time-sensitive healthcare applications such as remote patient monitoring and emergency response systems.

ISSN

2045-2322

Publisher

Springer Science and Business Media LLC

Volume

15

Issue

1

Disciplines

Computer Sciences

Keywords

Data security, Edge computing, Fog computing, Internet of medical things (IoMT), Latency reduction

Scopus ID

105010703927

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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