Leveraging IoT and Fog Computing in Healthcare Systems

Awaisi, Kamran Sattar; Hussain, Shahid; Ahmed, Mansoor; Khan, Arif; Ahmed, Ghufran

doi:10.1109/iotm.0001.1900096

Cited by 77 publications

(32 citation statements)

References 13 publications

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“…Fog computing extends the cloud by migrating data processing closer to the production site, thereby accelerating the system’s responsiveness to events. The Fog infrastructure allows the management of multiple data from IoMT devices that send the information to the Fog node reducing the latency, response time or data delay [ 32 , 33 , 34 ]. Several low-cost applications using IoT and Fog devices can be found in the literature in order to streamline diagnostics.…”

Section: State Of the Artmentioning

confidence: 99%

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Rincon

Guerra-Ojeda

Carrascosa

et al. 2020

Sensors

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Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.

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Section: State Of the Artmentioning

confidence: 99%

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Rincon

Guerra-Ojeda

Carrascosa

et al. 2020

Sensors

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“…Realizing the limitations of healthcare IoT applications based on the Cloud, Awaisi et al [ 41 ] proposed an efficient healthcare IoT based on fog computing to enhance efficiency and security. Specifically, they proposed an efficient healthcare IoT architecture based on fog computing.…”

Section: Survey Of Recent Iot Healthcare Applicationsmentioning

confidence: 99%

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

Quy

Nguyen

Anh

et al. 2021

Complex Intell. Syst.

106

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The history of human development has proven that medical and healthcare applications for humanity always are the main driving force behind the development of science and technology. The advent of Cloud technology for the first time allows providing systems infrastructure as a service, platform as a service and software as a service. Cloud technology has dominated healthcare information systems for decades now. However, one limitation of cloud-based applications is the high service response time. In some emergency scenarios, the control and monitoring of patient status, decision-making with related resources are limited such as hospital, ambulance, doctor, medical conditions in seconds and has a direct impact on the life of patients. To solve these challenges, optimal computing technologies have been proposed such as cloud computing, edge computing, and fog computing technologies. In this article, we make a comparison between computing technologies. Then, we present a common architectural framework based on fog computing for Internet of Health Things (Fog-IoHT) applications. Besides, we also indicate possible applications and challenges in integrating fog computing into IoT Healthcare applications. The analysis results indicated that there is huge potential for IoHT applications based on fog computing. We hope, this study will be an important guide for the future development of fog-based Healthcare IoT applications.

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“…11,12 In this manner, IoT technology has changed the traditional architectures of monitoring procedures. [13][14][15][16][17] In addition to this, time-sensitive statistic compilation has extended the scope of real-time monitoring of an individual under observation. In this manner, IoT-assisted sensing devices have made it possible to observe both macroscopic and microscopic health and environmental events.…”

Section: Research Domainmentioning

confidence: 99%

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

Manocha

2021

Trans Emerging Tel Tech

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Indoor environment pollution is one of the significant concerns related to the individual's health of every category. As a majority of the population is spending their time indoors, the research on environment monitoring is realized on priority. In this research, the correlation between the indoor environment and health is determined to predict the health severity of pregnant females in real-time. The proposed solution employs IoT-enabled smart sensors for data acquisition. The accumulated data is processed over the fog-cloud platform for data quantification. To classify the irregular events from the processed data, the machine learning-assisted Weighted-Naïve Bayes (W-NB) classification technique is deployed on the fog layer. Furthermore, a deep learning-inspired multilayered convolutional neural network-long short-term memory (M-CNN-LSTM) model is proposed to determine the scale of health severity. Moreover, a two-phased alert generation mechanism is proposed to deal with the case of a medical emergency by generating notifications to the concerned medical specialists. The performance of the system is validated by performing several experimental simulations over the environmental and health datasets comprised of nearly 38 356 instances. The performance of the proposed solution is evaluated in terms of event classification, severity prediction, temporal delay efficiency, and overall performance stability. The calculated outcomes registered considerable performance over the several state-of-the-art decision-making approaches.

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Leveraging IoT and Fog Computing in Healthcare Systems

Cited by 77 publications

References 13 publications

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

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