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- W4286375059 abstract "The connection between environmental monitoring and the Internet of Things (IoT) raises new possibilities for data transmission from environmental wireless sensors networks (EWSN) and transferring parameters of interest to a cloud. EWSN uses a low-power wide-area networks (LPWAN) which allow only very limited data throughput and are subject to regional restriction. The paper investigates a self-learning wavelet compression algorithm driven by a strategy based on Q-learning (QL). This approach allows optimiation of the total amount of transmitted data by applying lossy wavelet transform compression. The aim of this research is to achieve optimal use of the available communication channel width and minimize loss of information using compression. The paper presents a simulation-based study with design methodology for a QL controller. The results showed that the loss of information due to lossy compression causes a relative error in range of 0.3–1.7 % for most of the environmental parameters. The results also revealed that lossy compression causes small errors in parameters which experience slow and infrequent changes." @default.
- W4286375059 created "2022-07-21" @default.
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- W4286375059 date "2022-01-01" @default.
- W4286375059 modified "2023-09-30" @default.
- W4286375059 title "Environmental Monitoring Stations Data Transmission Using Reinforcement Learning Wavelet Compression Method" @default.
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- W4286375059 doi "https://doi.org/10.1016/j.ifacol.2022.06.021" @default.
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