ArticleScientific reports2023
DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 47 citations in OpenAlex.
- Pearson correlation-based clustering with collaborative task allocation in 5G Industrial Internet of Things divergent health networks.Scientific reports · 2025Article
- Rehabilomics Strategies Enabled by Cloud-Based Rehabilitation: Scoping Review.Journal of medical Internet research · 2025Article
- Blockchain-enabled verification of medical records using soul-bound tokens and cloud computing.Scientific reports · 2024Article
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Authors and funding
6 authors at 4 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.