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Permissioned Blockchain and Deep Reinforcement Learning Enabled Security and Energy Efficient Healthcare Internet of Things
Ieee Access ; 10:53640-53651, 2022.
Article in English | English Web of Science | ID: covidwho-1883114
ABSTRACT
Recently, the Healthcare Internet of Things (H-IoT) has been widely applied to alleviate the global challenge of the coronavirus disease 2019 (COVID-19) pandemic. However, security and limited energy capacity issues remain the two main factors that prevent the large-scale application of the H-IoT. Therefore, a permissioned blockchain and deep reinforcement learning (DRL)-empowered H-IoT system is presented in this research to address these two issues. The proposed H-IoT system can provide real-time security and energy-efficient healthcare services to control the propagation of the COVID-19 pandemic. To address the security issue, a permissioned blockchain method is adopted to guarantee the security of the proposed H-IoT system. As for handling the limited energy constraint, we employ the mobile edge computing (MEC) method to offload the computing tasks to alleviate the computational burden and energy consumption of the proposed H-IoT system. We also adopt an energy harvesting method to improve performance. In addition, a DRL method is employed to jointly optimize both the security and energy efficiency performance of the proposed system. The simulation results demonstrate that the proposed solution can balance the requirements of security and energy efficiency issues and hence can better respond to the COVID-19 pandemic.
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Full text: Available Collection: Databases of international organizations Database: English Web of Science Language: English Journal: Ieee Access Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: English Web of Science Language: English Journal: Ieee Access Year: 2022 Document Type: Article