FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis

Verma, Parag; Tiwari, Rajeev; Hong, Wei‐Chiang; Upadhyay, Shuchi; Yeh, Yi‐Hsuan

doi:10.1109/access.2022.3143793

Cited by 65 publications

(32 citation statements)

References 43 publications

(52 reference statements)

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“…In this scenario, the Fog node, in the form of a Raspberry Pi, is responsible for user authentication, feature extraction, classification of collected data using a Bayesian Belief Network (BBN), and issuing alerts to health practitioners if a critical event is detected. On the other hand, the authors of [ 49 ] developed an integrated environment that incorporates Deep Learning (DL) algorithms in Fog nodes for an application for coronary disease monitoring and diagnosis. This application has two types of Fog nodes, namely, broker and worker nodes, to distribute the computational tasks.…”

Section: State-of-the-artmentioning

confidence: 99%

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications

Shumba

Montanaro

Sergi

et al. 2022

Sensors

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Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.

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Section: State-of-the-artmentioning

confidence: 99%

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications

Shumba

Montanaro

Sergi

et al. 2022

Sensors

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“…In [20] authors proposed a framework FETCH: A Fog Enabled Technique for Clinical Healthcare system that integrates IoT, edge computing, and deep learning to monitor heart disease patients. Ali and Ghazal [21] proposed a framework to collect the patients’ data through cell phones via voice control to enable patients’ health data.…”

Section: Related Workmentioning

confidence: 99%

“… Domain Contribution 1 [19] Use of UAVs and Drones for fighting against the pandemic Application of LoRaWAN technology is used for monitoring COVID-19 patients remotely. 2 [20] IoT for the diagnosis of COVID-19 patients WeChat application is used for improving the coordination between diagnosis and treatment of COVID-19 patients. 3 [21] IoT for the services of telemedicine in COVID-19, wearable technologies for the prediction of COVID-19 enabled by IoT Demonstration of monitoring wearable devices remotely in COVID-19.…”

Section: Iot Technologies In Healthcare During the Covid-19 Pandemicmentioning

confidence: 99%

Applications of cognitive internet of medical things in modern healthcare

Jabbar¹,

Shandilya²,

Kumar³

et al. 2022

Computers and Electrical Engineering

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“…Healthy systems require computational operations to be performed on huge volumes of data. Parag Verma et al [10] in 2022 came up with an innovative research proposal for a fogenabled cloud computing framework that makes use of FogBus. This framework demonstrates utility in the form of consumption of power, network bandwidth, jitter, latency, and process execution time as well as accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A State-of-the-Art Technique for Malware Detection Based on Deep Learning in IoT Environment

Ali

Wang

Hou

et al. 2022

Preprint

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Internet of Things (IoT) devices are used to sense, gather, transmit and control data. IoT improves user experiences by connecting and sharing information across smart devices. Malware attacks easily target IoT-connected consoles to get control or damage privacy. Effective IoT device protection might rescue millions of internet users from malware. Malware detection methods are computationally expensive and complex. Many different machine learning models were used to deduct malware or viruses. As innovation advances, threat intelligence and malware researchers struggle. Traditional security measures like firewalls and Intrusion Detection Systems (IDS) must be changed to work with the current IoT models. Also, the Internet of Things and Cloud/Fog Computing go well together. They are often used interchangeably when talking about technical services and work together to make IoT services more complete. This study suggests deep learning-based approaches for detecting IoT malware in order to prevent harm to IoT devices. The proposed algorithms AlexNet, ResNet-18, and Convolution Neural Network (CNN) are applied to the well-known public dataset "Mal_Img". The experimental findings show that the accuracy of the suggested method is superior to that of earlier state-of-the-art and deep learning methods. The highest attained accuracies for proposed methods proposed algorithms AlexNet, ResNet-18, and Convolution Neural Network are 97.74%, 88.74%, and 98.02%. Our framework has currently been demonstrated to be a useful research tool for the BitDefender AntiMalware Research Labs' computer security specialists. In the future, more advanced malware (polymorphic and metamorphic) can be detected using the same models.

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FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis

Cited by 65 publications

References 43 publications

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications

Applications of cognitive internet of medical things in modern healthcare

A State-of-the-Art Technique for Malware Detection Based on Deep Learning in IoT Environment

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