Fog and Cloud Computing-based IoT in Healthcare Monitoring System for Healthy Ageing

Tudora, Eleonora; Tîrziu, Eugenia; Nicolau, Nicolae-Dragos; Gheorghe-Moisii, Maria

doi:10.1109/cscs52396.2021.00086

Cited by 4 publications

(1 citation statement)

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“…An optimized neural network with an accuracy of 97% can be used in an IoT system to detect fall. Authors in [24], propose an approach that combines IoT, fog, and cloud computing technologies in a healthcare monitoring system for healthy aging in a familiar setting to improve quality of life. In [15], authors predict and analyze indoor air quality, which is important in the case of infected people in quarantine using IoT technology and a machine learning model with high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients

Saidi

Kacem

Tounsi

et al. 2022

Lecture Notes in Computer Science

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The coronavirus known as COVID-19 is the topic of the hour all over the world. This virus has invaded the world with its invariants, which are characterized by their rapid spread. COVID-19 has impacted the health of people and the economy of countries. For that, laboratories, researchers, and doctors are in a race against time to find a cure for this pandemic. To combat this virus, cutting-edge technologies such as artificial intelligence, cloud computing, and big data have been put in place. In our work, we use Internet of Things (IoT) technology. The use of IoT in an efficient way can lead to detecting infected people and avoiding being contaminated. In this paper, we are interested in the remote medical monitoring of patients who have tested positive for COVID-19. We propose a meta-modeling technique to model the IoT architecture. Then we implement two IoT solutions that permit the remote medical monitoring of patients infected with COVID-19 and the respect of social distancing by instantiating correct models that conform to the proposed meta-model in order to mitigate the COVID-19 outbreak.

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Section: Literature Reviewmentioning

confidence: 99%