Environmental Monitoring Stations Data Transmission Using Reinforcement Learning Wavelet Compression Method
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250220" target="_blank" >RIV/61989100:27240/22:10250220 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2405896322003366" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405896322003366</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2022.06.021" target="_blank" >10.1016/j.ifacol.2022.06.021</a>
Alternative languages
Result language
angličtina
Original language name
Environmental Monitoring Stations Data Transmission Using Reinforcement Learning Wavelet Compression Method
Original language description
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. Copyright (C) 2022 The Authors.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IFAC PapersOnLine. Volume 55
ISBN
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ISSN
2405-8963
e-ISSN
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Number of pages
6
Pages from-to
127-132
Publisher name
Elsevier
Place of publication
Amsterdam
Event location
Sarajevo
Event date
May 17, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000836230600021