Enhancing Healthcare Efficacy Through IoT-Edge Fusion: A Novel Approach for Smart Health Monitoring and Diagnosis

This paper presents an innovative framework that leverages cutting-edge technologies to revolutionize healthcare systems, focusing on data security, privacy, and efficient medical diagnosis. Our approach integrates distributed ledger technology (DLT), artificial intelligence (AI), and edge computing...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.136456-136467
Main Authors: Izhar, Muhammad, Naqvi, Syed Asad Ali, Ahmed, Adeel, Abdullah, Saima, Alturki, Nazik, Jamel, Leila
Format: Article
Language:English
Subjects:
Artificial intelligence
Cloud computing
Computational modeling
Computer architecture
Computer systems
Cryptography
Curves
Cybersecurity
Data collection
Data exchange
Data processing
Diagnosis
Distributed ledger
Edge computing
Health care
Hybrid systems
Internet of Things
IoT
Machine learning
Medical electronics
Medical services
Monitoring
Nodes
Peer to peer computing
Privacy
Telemedicine
wireless sensor network
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Summary:This paper presents an innovative framework that leverages cutting-edge technologies to revolutionize healthcare systems, focusing on data security, privacy, and efficient medical diagnosis. Our approach integrates distributed ledger technology (DLT), artificial intelligence (AI), and edge computing to create a robust and dependable medical ecosystem. In our proposed system, patients' health data is securely managed using a combination of elliptic curve cryptography-based identity-based cryptosystems and edge nodes, ensuring both privacy and integrity. These edge nodes, designed for low-power and short-range communication, play a pivotal role in in-vivo data collection and monitoring within the human body. The DLT model at the core of our framework utilizes peer-to-peer networks, enabling seamless information exchange while eliminating the need for centralized servers. We emphasize public edge DLTs, such as Ethereum, to ensure accessibility and data ownership for all stakeholders. Furthermore, our system incorporates a hybrid machine learning model for early detection and prediction of security threats, enhancing overall system efficiency. Our findings demonstrate a remarkable 99.7% accuracy in classification using this approach. In conclusion, this framework's multidisciplinary approach bridges the gap between healthcare, edge computing, and DLT, promising real-time data processing, enhanced security, and privacy preservation. With the rise of the Internet of Things, this innovation holds the potential to transform the future of healthcare technology.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3337092