DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system

【Author】 Lakhan, Abdullah; Mohammed, Mazin Abed; Nedoma, Jan; Martinek, Radek; Tiwari, Prayag; Kumar, Neeraj

【Source】SCIENTIFIC REPORTS

【影响因子】4.996

【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.

【Keywords】

【发表时间】2023 13-Mar

【收录时间】2023-07-09

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-医疗领域