DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255229" target="_blank" >RIV/61989100:27240/24:10255229 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10602504" target="_blank" >https://ieeexplore.ieee.org/document/10602504</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JSYST.2024.3424259" target="_blank" >10.1109/JSYST.2024.3424259</a>
Alternative languages
Result language
angličtina
Original language name
DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows
Original language description
Digital healthcare has garnered much attention from academia and industry for health and well-being. Many digital healthcare architectures based on large-scale edge and cloud multiagent systems (LSMASs) have recently been presented. The LSMAS allows agents from different institutions to work together to achieve healthcare processing goals for users. This article presents a digital twin large-scale multiagent strategy (DT-LSMAS) comprising mobile, edge, and cloud agents. The DT-LSMAS comprised different schemes for healthcare workflows, such as added healthcare workflows, application partitioning, and scheduling. We consider healthcare workflows with different biosensor data such as heartbeat, blood pressure, glucose monitoring, and other healthcare tasks. We partitioned workflows into mobile, edge, and cloud agents to meet the deadline, total time, and security of workflows in large-scale edge and cloud nodes. To handle the large-scale resource for real-time sensor data, we suggested digital twin-enabled edge nodes, where delay-sensitive workflow tasks are scheduled and executed under their quality of service requirements. Simulation results show that the DT-LSMAS outperformed in terms of total time by 50%, minimizing the risk of resource leakage and deadline missing during scheduling on heterogeneous nodes. In conclusion, the DT-LSMAS obtained optimal results for workflow applications.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Systems Journal
ISSN
1932-8184
e-ISSN
1937-9234
Volume of the periodical
2024
Issue of the periodical within the volume
July 2024
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
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UT code for WoS article
001273001200001
EID of the result in the Scopus database
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