Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management

Advances in health-related behaviors, technical breakthroughs, and the spread of healthy living activities have expanded personal health evaluation on a broad scale. In medical applications, networks, data, and communication systems are extensively utilized and further expanding. Real-time applicati...

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Main Authors: Bazeer Ahamed, Suseela Sellamuthu, Purushotam Naidu Karri, Inumarthi V. Srinivas, A.N. Mohammed Zabeeulla, M. Ashok Kumar
Format: Article
Language:English
Published: Elsevier 2023-12-01
Series:Measurement: Sensors
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2665917423002647
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author Bazeer Ahamed
Suseela Sellamuthu
Purushotam Naidu Karri
Inumarthi V. Srinivas
A.N. Mohammed Zabeeulla
M. Ashok Kumar
author_facet Bazeer Ahamed
Suseela Sellamuthu
Purushotam Naidu Karri
Inumarthi V. Srinivas
A.N. Mohammed Zabeeulla
M. Ashok Kumar
author_sort Bazeer Ahamed
collection DOAJ
description Advances in health-related behaviors, technical breakthroughs, and the spread of healthy living activities have expanded personal health evaluation on a broad scale. In medical applications, networks, data, and communication systems are extensively utilized and further expanding. Real-time applications for remote health monitoring generate massive amounts of data in a short period. Internet of Things (IoT) and cloud systems, resulting in unprocessed and inadequate data for end-users. Since cloud technology is a dispersed intermediary layer between the edge network and the cloud infrastructure, the delay is significantly decreased, and reliable communications can be established using cloud computing on an IoT-enabled healthcare device. This study presents an Energy Efficient Healthcare Data Management Method (EE-HDMM), which enhances health monitoring using an IoT-assisted wearable sensor infrastructure. The difficulty of constrained sources and the energy consumption is minimized by proposing the optimum energy resource allocation method. Wearable sensor technology supported by cloud computing is the ideal and dependable platform for powerful life-critical systems that are unlikely to have communication delays. Compared to conventional techniques, the suggested EE-HDMM has a high precision rate of 97%, a sensitivity rate of 94%, and a system efficiency of 96%.
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spelling doaj.art-4c179ed562a44dc99787a30e7c78cc2f2023-11-26T05:13:52ZengElsevierMeasurement: Sensors2665-91742023-12-0130100928Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data managementBazeer Ahamed0Suseela Sellamuthu1Purushotam Naidu Karri2Inumarthi V. Srinivas3A.N. Mohammed Zabeeulla4M. Ashok Kumar5Department of IT, University of Technology And Applied Sciences Al Musanna, Sultanate of Oman; Corresponding author.Senior Grade, School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, TamilNadu, IndiaDepartment of CSE, Gayatri Vidya Parishad College of Engineering for Women, Visakhapatnam, Andhra Pradesh, 535546, IndiaDepartmentof Data Science and Technology, K.J. Somaiya Institute of Management, Vidya Nagar, Vidya vihar (East), Mumbai, 400077, IndiaComputer Science and Engineering, JAIN(Deemed-to-be University), Bangalore, IndiaDepartment of Computer Science and Software Engineering, Skyline University Nigeria.Kano, NigeriaAdvances in health-related behaviors, technical breakthroughs, and the spread of healthy living activities have expanded personal health evaluation on a broad scale. In medical applications, networks, data, and communication systems are extensively utilized and further expanding. Real-time applications for remote health monitoring generate massive amounts of data in a short period. Internet of Things (IoT) and cloud systems, resulting in unprocessed and inadequate data for end-users. Since cloud technology is a dispersed intermediary layer between the edge network and the cloud infrastructure, the delay is significantly decreased, and reliable communications can be established using cloud computing on an IoT-enabled healthcare device. This study presents an Energy Efficient Healthcare Data Management Method (EE-HDMM), which enhances health monitoring using an IoT-assisted wearable sensor infrastructure. The difficulty of constrained sources and the energy consumption is minimized by proposing the optimum energy resource allocation method. Wearable sensor technology supported by cloud computing is the ideal and dependable platform for powerful life-critical systems that are unlikely to have communication delays. Compared to conventional techniques, the suggested EE-HDMM has a high precision rate of 97%, a sensitivity rate of 94%, and a system efficiency of 96%.http://www.sciencedirect.com/science/article/pii/S2665917423002647Internet of thingsWearable sensorsHealthcare data managementCloud computing
spellingShingle Bazeer Ahamed
Suseela Sellamuthu
Purushotam Naidu Karri
Inumarthi V. Srinivas
A.N. Mohammed Zabeeulla
M. Ashok Kumar
Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management
Measurement: Sensors
Internet of things
Wearable sensors
Healthcare data management
Cloud computing
title Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management
title_full Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management
title_fullStr Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management
title_full_unstemmed Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management
title_short Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management
title_sort design of an energy efficient iot device assisted wearable sensor platform for healthcare data management
topic Internet of things
Wearable sensors
Healthcare data management
Cloud computing
url http://www.sciencedirect.com/science/article/pii/S2665917423002647
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