Permissioned Blockchain and Deep Reinforcement Learning Enabled Security and Energy Efficient Healthcare Internet of Things

【Author】 Liu, Long; Li, Zhichao

【Source】IEEE ACCESS

【影响因子】3.476

【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.

【Keywords】COVID-19; Blockchains; Security; Pandemics; Medical services; Internet of Things; Industries; Blockchain; deep reinforcement learning; mobile edge computing; energy harvesting; healthcare Internet of Things; COVID-19

【发表时间】2022

【收录时间】2022-06-06

【文献类型】实证性文章

【主题类别】

区块链应用-实体经济-医疗领域