Framework for a Secure and Sustainable Internet of Medical Things, Requirements, Design Challenges, and Future Trends

Author:

Villegas-Ch William1ORCID,García-Ortiz Joselin1ORCID,Urbina-Camacho Isabel2

Affiliation:

1. Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito 170125, Ecuador

2. Facultad de Filosofía, Letras y Ciencias de la Educación, Universidad Central del Ecuador, Quito 170129, Ecuador

Abstract

The framework presented in this article provides a guide for designing secure and sustainable internet of medical things (IoMT) solutions. The main objective is to address the challenges related to safety and sustainability in the medical field. The critical conditions driving these challenges are identified, and future trends in the field of IoMT are discussed. To assess the effectiveness of the proposed framework, a case study was carried out in a private medical clinic. In this study, an IoMT system was implemented to monitor patients’ vital signs, even when they were not in the clinic. The positive results demonstrated that the implemented IoMT system met the established security and sustainability requirements. The main statistical findings of the case study include the real-time monitoring of the vital signs of the patients, which improved the quality of care and allowed for the early detection of possible complications. In addition, medical devices such as the blood pressure monitor, pulse oximeter, and electrocardiograph were selected, proving safe, durable, and energy and maintenance efficient. These results were consistent with previous research that had shown the benefits of IoMT in remote monitoring, the early detection of health problems, and improved medical decision-making.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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2. IoMT Future Trends and Challenges;Advances in Healthcare Information Systems and Administration;2024-05-17

3. A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems;Scientific Reports;2024-05-04

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