FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis
These days cloud-based infrastructure is facing many challenges, out of which the major issue is their syncing data before cutover and data migration. Due to the limited scalability in terms of security concerns of cloud computing, the need for a centralized IoTs based environment has been constrain...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9682727/ |
| _version_ | 1797243417729695744 |
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| author | Parag Verma Rajeev Tiwari Wei-Chiang Hong Shuchi Upadhyay Yi-Hsuan Yeh |
| author_facet | Parag Verma Rajeev Tiwari Wei-Chiang Hong Shuchi Upadhyay Yi-Hsuan Yeh |
| author_sort | Parag Verma |
| collection | DOAJ |
| description | These days cloud-based infrastructure is facing many challenges, out of which the major issue is their syncing data before cutover and data migration. Due to the limited scalability in terms of security concerns of cloud computing, the need for a centralized IoTs based environment has been constrained to a limited extent. The sensitivity of device latency emerged during healthy systems such as health monitoring, etc. is the main reason, because healthy systems require computing operations on high-volume data. Fog computing provides an innovative solution to improve the performance of cloud computing, providing the ability to take the necessary resources and those that are closer to the end-users. Existing fog computing models retain several limitations, such as either considering result accuracy or overestimating response time, but managing both together impairs system compatibility. FETCH is a proposed framework that integrates with edge computing devices to work on deep learning technology and automated monitoring and offers a highly useful framework for real-life health care systems such as heart disease and more. The proposed Fog-enabled cloud computing framework uses FogBus, which demonstrates utility in the form of consumption of power, network bandwidth, jitter, latency, process execution time, and their accuracy as well. |
| first_indexed | 2024-04-24T18:54:47Z |
| format | Article |
| id | doaj.art-2514b7f066c149628dc7e0166eed174f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:47Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2514b7f066c149628dc7e0166eed174f2024-03-26T17:43:17ZengIEEEIEEE Access2169-35362022-01-0110125481256310.1109/ACCESS.2022.31437939682727FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and DiagnosisParag Verma0Rajeev Tiwari1https://orcid.org/0000-0002-8245-4748Wei-Chiang Hong2https://orcid.org/0000-0002-3001-2921Shuchi Upadhyay3Yi-Hsuan Yeh4Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaDepartment of Systemics Cluster, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, IndiaDepartment of Information Management, Asia Eastern University of Science and Technology, New Taipei, TaiwanDepartment of Allied Health Sciences, School of Health Science and Technology, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, IndiaDepartment of Information Management, Asia Eastern University of Science and Technology, New Taipei, TaiwanThese days cloud-based infrastructure is facing many challenges, out of which the major issue is their syncing data before cutover and data migration. Due to the limited scalability in terms of security concerns of cloud computing, the need for a centralized IoTs based environment has been constrained to a limited extent. The sensitivity of device latency emerged during healthy systems such as health monitoring, etc. is the main reason, because healthy systems require computing operations on high-volume data. Fog computing provides an innovative solution to improve the performance of cloud computing, providing the ability to take the necessary resources and those that are closer to the end-users. Existing fog computing models retain several limitations, such as either considering result accuracy or overestimating response time, but managing both together impairs system compatibility. FETCH is a proposed framework that integrates with edge computing devices to work on deep learning technology and automated monitoring and offers a highly useful framework for real-life health care systems such as heart disease and more. The proposed Fog-enabled cloud computing framework uses FogBus, which demonstrates utility in the form of consumption of power, network bandwidth, jitter, latency, process execution time, and their accuracy as well.https://ieeexplore.ieee.org/document/9682727/Fog computingedge computinghealthcaremachine learning (ML)deep learning (DL)Internet of Things (IoT) |
| spellingShingle | Parag Verma Rajeev Tiwari Wei-Chiang Hong Shuchi Upadhyay Yi-Hsuan Yeh FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis IEEE Access Fog computing edge computing healthcare machine learning (ML) deep learning (DL) Internet of Things (IoT) |
| title | FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis |
| title_full | FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis |
| title_fullStr | FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis |
| title_full_unstemmed | FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis |
| title_short | FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis |
| title_sort | fetch a deep learning based fog computing and iot integrated environment for healthcare monitoring and diagnosis |
| topic | Fog computing edge computing healthcare machine learning (ML) deep learning (DL) Internet of Things (IoT) |
| url | https://ieeexplore.ieee.org/document/9682727/ |
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