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- W3132013322 abstract "This paper proposes a high-performance Internet of Things (IoT) middleware based on reinforcement learning. The adaptive decision-maker is realized by the K-armed bandit algorithm, which can dynamically resize the server accept queue. This paper adopts service-oriented architecture(SOA) to make the middleware architecture be loosely-coupled, reusable, and easily extensible. Through standardized service interfaces, users can transparently interact with heterogeneous IoT devices. In addition, the high availability of the middleware platform is achieved through non-blocking IO, caching, and message queuing technologies. Through the experiments over Apache JMeter, the results verify that our IoT middleware has a better performance under high concurrency scenarios." @default.
- W3132013322 created "2021-03-01" @default.
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- W3132013322 date "2020-12-11" @default.
- W3132013322 modified "2023-09-23" @default.
- W3132013322 title "A High-performance IoT Middleware Based on Reinforcement Learning" @default.
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- W3132013322 doi "https://doi.org/10.1109/iccc51575.2020.9345234" @default.
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