A Comprehensive Overview of Fog Data Processing and Analytics for Healthcare 4.0

Krishnamurthi, Rajalakshmi; Gopinathan, Dhanalekshmi; Nayyar, Anand

doi:10.1007/978-3-030-46197-3_5

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…The data collected can be transmitted to centralized data consolidation servers hosted in end computing systems of hospital networks popularly known as fog computing ( [16], [17], [18], [19], [20]). Fog computing is a virtualized computing and data storage architecture deployed in edge computing infrastructures hosted by the participating organization and integrated through Internet for collaborating computing.…”

Section: Literature Review On Current Requirements and Opportunities ...mentioning

confidence: 99%

Technology for Predictive Health Monitoring of Elderly and Vulnerable Citizens in Kenya using Healthcare 4.0: A Theoretical Review

Barasa,

Etene

2023

IJCSMC

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The traditional critical healthcare planning is based on periodic routine checks and a reactive strategy of interventions whenever the individuals have entered high risk states (such as heart attacks or strokes). The Healthcare 4.0 design is based on the principle of continuous predictive health monitoring such that the buildup process towards high risk states of individuals can be identified much before their critical levels thus ensuring proactive interventions by the hospitals. Kenya has significant potential to adopt Healthcare 4.0 technology using traditional information and networking components. This research investigates how Healthcare 4.0 can be used in predictive health monitoring of the elderly and vulnerable citizens that individual hospitals can adopt for operational excellence. Desktop review methodology was used. The technical literature review was used to define an operations algorithm. The results suggest that several customizations are needed in the traditional information and networking components to make them fit for predictive health monitoring. The individuals under critical monitoring should not be allowed high mobility as it has a negative effect on monitoring reliability and effectiveness. Healthcare 4.0 adoption in Kenya is feasible. Its operational details need to be defined with several tiers of customized solutions for different patient classes. There are some limitations found in this research, which should be kept in mind while designing and operating Healthcare 4.0 networks in Kenya.

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Section: Literature Review On Current Requirements and Opportunities ...mentioning

confidence: 99%

Technology for Predictive Health Monitoring of Elderly and Vulnerable Citizens in Kenya using Healthcare 4.0: A Theoretical Review

Barasa,

Etene

2023

IJCSMC

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“…Based on the experimental results, the efficiency of low privacy leakage and high data integrity in the IoT-Healthcare is monitored, recorded, and analyzed (4) The prototype system is designed to ensure data analytics [13] and privacy preservation using deep learning. The nonprivacy data extraction algorithm is used for separating privacy information, and the health-related data is analyzed To avoid overfitting [14], a data augmentation methodology is used.…”

Section: Introductionmentioning

confidence: 99%

Secure IoT Healthcare Architecture with Deep Learning‐Based Access Control System

Thilagam¹,

Beno²,

Lakshmi

et al. 2022

Journal of Nanomaterials

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The existing healthcare system based on traditional management involves the storage and processing of large quantities of medical data. The incorporation of the Internet of Things (IoT) and its gradual maturation has led to the evolution of IoT-enabled healthcare with extraordinary data processing capability and massive data storage. Due to the advancement in the Industrial Internet of Things (IIoT), the resulting system is aimed at building an intelligent healthcare system that can monitor the medical health of the patient by means of a wearable device that is monitored remotely. The data that is gathered by the wearable IoT module is stored in the cloud server which is subject to privacy leakage and attacks by unauthorized users and attackers. To address this security issue, an IoT-based deep learning-based privacy preservation and data analytics system is proposed in this work. Data is collected from the user, and the sensitive information is segregated and separated. Using a convolutional neural network (CNN), the health-related information is analyzed in the cloud, devoid of users’ privacy information. Thus, a secure access control module is introduced that works based on the user attributes for the IoT-Healthcare system. A relationship between the users’ trust and attributes is discovered using the proposed work. The precision, recall, and F1 score of the proposed CNN classifier are achieved at 95%. With the increase in the size of the training set, higher performance is attained. When data augmentation is added, the system performs better without data augmentation. Further, the accuracy of around 98% is achieved with an increased user count. Experimental analysis indicates the robustness and effectiveness of the proposed system with respect to low privacy leakage and high data integrity.

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Novel Designs of Smart Healthcare Systems: Technologies, Architecture, and Applications

Ilmudeen

Nayyar

2022

Machine Learning for Critical Internet of Medical Things

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A Comprehensive Overview of Fog Data Processing and Analytics for Healthcare 4.0

Cited by 7 publications

References 28 publications

Technology for Predictive Health Monitoring of Elderly and Vulnerable Citizens in Kenya using Healthcare 4.0: A Theoretical Review

Technology for Predictive Health Monitoring of Elderly and Vulnerable Citizens in Kenya using Healthcare 4.0: A Theoretical Review

Secure IoT Healthcare Architecture with Deep Learning‐Based Access Control System

Novel Designs of Smart Healthcare Systems: Technologies, Architecture, and Applications

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