EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems
With the wide application of mobile healthcare systems, the total amount of healthcare data is ever increasing rapidly as users interact with healthcare service providers frequently. This leads to a challenging task to manage healthcare data. Existing work mainly pay attention to centralized and blo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8637930/ |
| _version_ | 1818914710087532544 |
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| author | Xiaohuan Li Xumin Huang Chunhai Li Rong Yu Lei Shu |
| author_facet | Xiaohuan Li Xumin Huang Chunhai Li Rong Yu Lei Shu |
| author_sort | Xiaohuan Li |
| collection | DOAJ |
| description | With the wide application of mobile healthcare systems, the total amount of healthcare data is ever increasing rapidly as users interact with healthcare service providers frequently. This leads to a challenging task to manage healthcare data. Existing work mainly pay attention to centralized and blockchain-based mechanisms. But they cannot adapt to the increasing amount of global healthcare data and suffer from complex application challenges, respectively. Decentralized and collaborative data management assisted by edge computing exhibits major advantages in improving overall system performance. We present a secure and efficient data management system named as EdgeCare for mobile healthcare systems. Local authorities are established to schedule edge servers for processing healthcare data and facilitating data trading. A hierarchical architecture with collaboration is designed for feasible implementation of EdgeCare. After that, we investigate secure data uploading and sharing in the system. We use an electronic medical record to show how healthcare data is processed with security considerations. We also conduct the Stackelberg game-based optimization algorithm to approach the optimal incentive mechanism for a data collector and users in the fair decentralized data trading. The numerical results with security analysis are provided to demonstrate that EdgeCare offers effective solutions to protect healthcare data, and support efficient data trading. |
| first_indexed | 2024-12-19T23:50:42Z |
| format | Article |
| id | doaj.art-f6dfc18f48c644da8665a2bed7ac8eeb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:50:42Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f6dfc18f48c644da8665a2bed7ac8eeb2022-12-21T20:01:09ZengIEEEIEEE Access2169-35362019-01-017220112202510.1109/ACCESS.2019.28982658637930EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare SystemsXiaohuan Li0Xumin Huang1Chunhai Li2Rong Yu3https://orcid.org/0000-0002-9042-7256Lei Shu4School of Electronic and Information Engineering, Beihang University, Beijing, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing, ChinaWith the wide application of mobile healthcare systems, the total amount of healthcare data is ever increasing rapidly as users interact with healthcare service providers frequently. This leads to a challenging task to manage healthcare data. Existing work mainly pay attention to centralized and blockchain-based mechanisms. But they cannot adapt to the increasing amount of global healthcare data and suffer from complex application challenges, respectively. Decentralized and collaborative data management assisted by edge computing exhibits major advantages in improving overall system performance. We present a secure and efficient data management system named as EdgeCare for mobile healthcare systems. Local authorities are established to schedule edge servers for processing healthcare data and facilitating data trading. A hierarchical architecture with collaboration is designed for feasible implementation of EdgeCare. After that, we investigate secure data uploading and sharing in the system. We use an electronic medical record to show how healthcare data is processed with security considerations. We also conduct the Stackelberg game-based optimization algorithm to approach the optimal incentive mechanism for a data collector and users in the fair decentralized data trading. The numerical results with security analysis are provided to demonstrate that EdgeCare offers effective solutions to protect healthcare data, and support efficient data trading.https://ieeexplore.ieee.org/document/8637930/Public healthcarecollaborative workinternet of thingsdistributed management |
| spellingShingle | Xiaohuan Li Xumin Huang Chunhai Li Rong Yu Lei Shu EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems IEEE Access Public healthcare collaborative work internet of things distributed management |
| title | EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems |
| title_full | EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems |
| title_fullStr | EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems |
| title_full_unstemmed | EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems |
| title_short | EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems |
| title_sort | edgecare leveraging edge computing for collaborative data management in mobile healthcare systems |
| topic | Public healthcare collaborative work internet of things distributed management |
| url | https://ieeexplore.ieee.org/document/8637930/ |
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