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Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems
The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490659/ https://www.ncbi.nlm.nih.gov/pubmed/37687933 http://dx.doi.org/10.3390/s23177474 |
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author | Subramaniam, Erana Veerappa Dinesh Srinivasan, Kathiravan Qaisar, Saeed Mian Pławiak, Paweł |
author_facet | Subramaniam, Erana Veerappa Dinesh Srinivasan, Kathiravan Qaisar, Saeed Mian Pławiak, Paweł |
author_sort | Subramaniam, Erana Veerappa Dinesh |
collection | PubMed |
description | The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using Awad’s Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities. |
format | Online Article Text |
id | pubmed-10490659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104906592023-09-09 Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems Subramaniam, Erana Veerappa Dinesh Srinivasan, Kathiravan Qaisar, Saeed Mian Pławiak, Paweł Sensors (Basel) Article The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using Awad’s Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities. MDPI 2023-08-28 /pmc/articles/PMC10490659/ /pubmed/37687933 http://dx.doi.org/10.3390/s23177474 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Subramaniam, Erana Veerappa Dinesh Srinivasan, Kathiravan Qaisar, Saeed Mian Pławiak, Paweł Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems |
title | Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems |
title_full | Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems |
title_fullStr | Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems |
title_full_unstemmed | Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems |
title_short | Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems |
title_sort | interoperable iomt approach for remote diagnosis with privacy-preservation perspective in edge systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490659/ https://www.ncbi.nlm.nih.gov/pubmed/37687933 http://dx.doi.org/10.3390/s23177474 |
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