Double Q-learning Adaptive Wavelet Compression Method for Data Transmission at Environmental Monitoring Stations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251747" target="_blank" >RIV/61989100:27240/22:10251747 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10022080" target="_blank" >https://ieeexplore.ieee.org/document/10022080</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SSCI51031.2022.10022080" target="_blank" >10.1109/SSCI51031.2022.10022080</a>
Alternative languages
Result language
angličtina
Original language name
Double Q-learning Adaptive Wavelet Compression Method for Data Transmission at Environmental Monitoring Stations
Original language description
We present a Double Q-learning (DQL) algorithm to control the data wavelet compression levels in environmental wireless monitoring networks (EWNS). EWNS are commonly equipped with low-power wide-area network (LPWAN) modules with the ability to transmit very small volumes of data. The presented method allows optimization at the edge computing level, thereby obtaining maximum utilization of the established communication channel. The study applies simulations in combination with a methodology designed to control the DQL strategy. The results indicate that the proposed computational intelligent method was able to deliver adaptive compression with zero buffer overflows while experiencing significant fluctuations in the communications throughput. (C) 2022 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
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
2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 : proceedings : 4-7 december 2022, Singapore
ISBN
978-1-66548-769-6
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
567-572
Publisher name
IEEE - Institute of Electrical and Electronics Engineers
Place of publication
Piscataway
Event location
Singapur
Event date
Dec 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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