Secure Edge of Things for Smart Healthcare Surveillance Framework
The vast development of the Internet of Things (IoT) and cloud-enabled data processing solutions provide the opportunity to build novel and fascinating smart, connected healthcare systems. Smart healthcare systems analyze the IoT-generated patient data to both enhance the quality of patient care and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8654187/ |
| _version_ | 1818428895612895232 |
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| author | Abdulatif Alabdulatif Ibrahim Khalil Xun Yi Mohsen Guizani |
| author_facet | Abdulatif Alabdulatif Ibrahim Khalil Xun Yi Mohsen Guizani |
| author_sort | Abdulatif Alabdulatif |
| collection | DOAJ |
| description | The vast development of the Internet of Things (IoT) and cloud-enabled data processing solutions provide the opportunity to build novel and fascinating smart, connected healthcare systems. Smart healthcare systems analyze the IoT-generated patient data to both enhance the quality of patient care and reduce healthcare costs. A major challenge for these systems is how the Cloud of Things can handle the data generated from billions of connected IoT devices. Edge computing infrastructure offers a promising solution by operating as a middle layer between the IoT devices and cloud computing. The Edge of Things (EoT) can offer small-scale real-time computing and storage capabilities that ensures low latency and optimal utilization of the IoT resources. However, the EoT has privacy-preservation issues, which is a significant concern for the healthcare systems that contain sensitive patient data. This paper introduces a novel EoT computing framework for secure and smart healthcare surveillance services. Fully homomorphic encryption preserves data privacy and is stored and processed within an EoT framework. A distributed approach for clustering-based techniques is developed for the proposed EoT framework with the scalability to aggregate and analyze the large-scale and heterogeneous data in the distributed EoT devices independently before it is sent to the cloud. We demonstrate the proposed framework by evaluating a case study for the patient biosignal data. Our framework rapidly accelerates the analysis response time and performance of the encrypted data processing while preserving a high level of analysis accuracy and data privacy. |
| first_indexed | 2024-12-14T15:08:53Z |
| format | Article |
| id | doaj.art-74de5d4187f8419e891b844e3eb4c786 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:08:53Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-74de5d4187f8419e891b844e3eb4c7862022-12-21T22:56:37ZengIEEEIEEE Access2169-35362019-01-017310103102110.1109/ACCESS.2019.28993238654187Secure Edge of Things for Smart Healthcare Surveillance FrameworkAbdulatif Alabdulatif0https://orcid.org/0000-0002-3167-8020Ibrahim Khalil1Xun Yi2Mohsen Guizani3Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi ArabiaSchool of Science, RMIT University, Melbourne, VIC, AustraliaSchool of Science, RMIT University, Melbourne, VIC, AustraliaDepartment of Electrical and Computer Engineering, University of Idaho, Moscow, ID, USAThe vast development of the Internet of Things (IoT) and cloud-enabled data processing solutions provide the opportunity to build novel and fascinating smart, connected healthcare systems. Smart healthcare systems analyze the IoT-generated patient data to both enhance the quality of patient care and reduce healthcare costs. A major challenge for these systems is how the Cloud of Things can handle the data generated from billions of connected IoT devices. Edge computing infrastructure offers a promising solution by operating as a middle layer between the IoT devices and cloud computing. The Edge of Things (EoT) can offer small-scale real-time computing and storage capabilities that ensures low latency and optimal utilization of the IoT resources. However, the EoT has privacy-preservation issues, which is a significant concern for the healthcare systems that contain sensitive patient data. This paper introduces a novel EoT computing framework for secure and smart healthcare surveillance services. Fully homomorphic encryption preserves data privacy and is stored and processed within an EoT framework. A distributed approach for clustering-based techniques is developed for the proposed EoT framework with the scalability to aggregate and analyze the large-scale and heterogeneous data in the distributed EoT devices independently before it is sent to the cloud. We demonstrate the proposed framework by evaluating a case study for the patient biosignal data. Our framework rapidly accelerates the analysis response time and performance of the encrypted data processing while preserving a high level of analysis accuracy and data privacy.https://ieeexplore.ieee.org/document/8654187/Smart healthcareInternet of Thingsedge computinghomomorphic encryption |
| spellingShingle | Abdulatif Alabdulatif Ibrahim Khalil Xun Yi Mohsen Guizani Secure Edge of Things for Smart Healthcare Surveillance Framework IEEE Access Smart healthcare Internet of Things edge computing homomorphic encryption |
| title | Secure Edge of Things for Smart Healthcare Surveillance Framework |
| title_full | Secure Edge of Things for Smart Healthcare Surveillance Framework |
| title_fullStr | Secure Edge of Things for Smart Healthcare Surveillance Framework |
| title_full_unstemmed | Secure Edge of Things for Smart Healthcare Surveillance Framework |
| title_short | Secure Edge of Things for Smart Healthcare Surveillance Framework |
| title_sort | secure edge of things for smart healthcare surveillance framework |
| topic | Smart healthcare Internet of Things edge computing homomorphic encryption |
| url | https://ieeexplore.ieee.org/document/8654187/ |
| work_keys_str_mv | AT abdulatifalabdulatif secureedgeofthingsforsmarthealthcaresurveillanceframework AT ibrahimkhalil secureedgeofthingsforsmarthealthcaresurveillanceframework AT xunyi secureedgeofthingsforsmarthealthcaresurveillanceframework AT mohsenguizani secureedgeofthingsforsmarthealthcaresurveillanceframework |