DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system
Abstract Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29170-2 |
| _version_ | 1797864932697440256 |
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| author | Abdullah Lakhan Mazin Abed Mohammed Jan Nedoma Radek Martinek Prayag Tiwari Neeraj Kumar |
| author_facet | Abdullah Lakhan Mazin Abed Mohammed Jan Nedoma Radek Martinek Prayag Tiwari Neeraj Kumar |
| author_sort | Abdullah Lakhan |
| collection | DOAJ |
| description | Abstract Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network. |
| first_indexed | 2024-04-09T22:59:55Z |
| format | Article |
| id | doaj.art-ce1f085225c748d784cf3e1b95236211 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T22:59:55Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ce1f085225c748d784cf3e1b952362112023-03-22T11:02:48ZengNature PortfolioScientific Reports2045-23222023-03-0113111510.1038/s41598-023-29170-2DRLBTS: deep reinforcement learning-aware blockchain-based healthcare systemAbdullah Lakhan0Mazin Abed Mohammed1Jan Nedoma2Radek Martinek3Prayag Tiwari4Neeraj Kumar5Department of Computer Science, Dawood University of Engineering and TechnologyCollege of Computer Science and Information Technology, University of AnbarDepartment of Telecommunications, VSB-Technical University of OstravaDepartment of Cybernetics and Biomedical Engineering, VSB-Technical University of OstravaSchool of Information Technology, Halmstad UniversityDepartment of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed University)Abstract Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network.https://doi.org/10.1038/s41598-023-29170-2 |
| spellingShingle | Abdullah Lakhan Mazin Abed Mohammed Jan Nedoma Radek Martinek Prayag Tiwari Neeraj Kumar DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system Scientific Reports |
| title | DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system |
| title_full | DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system |
| title_fullStr | DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system |
| title_full_unstemmed | DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system |
| title_short | DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system |
| title_sort | drlbts deep reinforcement learning aware blockchain based healthcare system |
| url | https://doi.org/10.1038/s41598-023-29170-2 |
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