期刊
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
卷 19, 期 3, 页码 2506-2520出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2022.3194806
关键词
Remote health monitoring; COVID-19; fog computing; containers; sensors
资金
- Natural Sciences and Engineering Research Council of Canada [NSERC-RGPIN2020-06622]
This paper proposes a reliable fog-based remote health monitoring framework operating under uncertain fog computing conditions. By using reinforcement learning and differential evolution-based algorithm, the framework maximizes the number of satisfied tasks and demonstrates superior reliability performance compared to benchmarks.
As the World is still facing the COVID-19 pandemic, several researchers and industry players have proposed technological solutions to help fight the pandemic and pave the way for post-pandemic era precautions. In this matter, the potential benefits of remote health monitoring have been brought back to the spotlight. Indeed, with current advances in wireless communications, core network virtualization, and computing architectures as enablers, consistently guaranteeing the stringent quality-of-service (QoS) requirements of remote health monitoring, e.g., ultra-low latency, may be achievable. Notably, the fog computing (FC) paradigm has been advocated as a potential solution for remote health monitoring. However, the unreliability of fog nodes in FC networks is a critical aspect often overlooked despite its significant impact on vital latency requirements. This paper proposes a reliable fog-based remote health monitoring framework operating under uncertain fog computing conditions. Specifically, we formulate the problem of assigning tasks of remote sensors attached to patients to their adequate applications deployed in fog nodes aiming to maximize the number of satisfied tasks with respect to the fog nodes' availability and communication latency constraints. Due to the problem's NP-hardness, we leverage a differential evolution-based algorithm enhanced by reinforcement learning to deploy applications in fog nodes. Numerical results demonstrate the superior reliability performance of our proposed solution, in terms of the average success ratio of tasks, compared to benchmarks. Specifically, our simulations show up to 60 % performance improvement compared to benchmarks in specific scenarios. Moreover, by investigating the impact of several key parameters, we identify a design trade-off between the number of fog nodes and the latter's intrinsic failure rates.
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