Self-learning Wavelet Compression Method for Data Transmission from Environmental Monitoring Stations with a Low Bandwidth IoT Interface

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-learning Wavelet Compression Method for Data Transmission from Environmental Monitoring Stations with a Low Bandwidth IoT Interface

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Self-learning Wavelet Compression Method for Data Transmission from Environmental Monitoring Stations with a Low Bandwidth IoT Interface

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - proceedings

ISBN

978-1-72819-048-8

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Orlando

Datum konání akce

5. 12. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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