Self-learning Wavelet Compression Method for Data Transmission from Environmental Monitoring Stations with a Low Bandwidth IoT Interface
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249507" target="_blank" >RIV/61989100:27240/21:10249507 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9660160" target="_blank" >https://ieeexplore.ieee.org/document/9660160</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SSCI50451.2021.9660160" target="_blank" >10.1109/SSCI50451.2021.9660160</a>
Alternative languages
Result language
angličtina
Original language name
Self-learning Wavelet Compression Method for Data Transmission from Environmental Monitoring Stations with a Low Bandwidth IoT Interface
Original language description
The Internet of Things concept raises the possibility of connecting monitoring stations to the Internet. In many cases, these devices are equipped with a wireless interface which allows the transmission of data through a low-power wide-area network (LPWAN). This type of network has a limited data throughput due to technological limitations and regional restrictions. There are many research challenges in maximizing the useful transmitted information through a limited transmission channel. The paper presents self-learning wavelet compression method controlled by Q-Learning (QL), which is able to optimize an amount of transmitted data using lossy compression. The aim is to use transmission channel throughput as effectively as possible without the loss of data. A QL agent selects an appropriate compression method according to buffer use and maintains this level at 70 %. The proposed method was tested on environmental historical data. The results showed that our method is able to use more than 96 % of the available transmission channel throughput with minimal data loss, even if the communications channel throughput experiences significant changes. (C) 2021 IEEE.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
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
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - proceedings
ISBN
978-1-72819-048-8
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway
Event location
Orlando
Event date
Dec 5, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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