Comparison of Edge Computing Methods for Environmental Monitoring IoT Sensors Using Neural Networks and Wavelet Transforms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F22%3A10251709" target="_blank" >RIV/61989100:27360/22:10251709 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/22:10251709 RIV/61989100:27240/22:10251709

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10022118" target="_blank" >https://ieeexplore.ieee.org/document/10022118</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SSCI51031.2022.10022118" target="_blank" >10.1109/SSCI51031.2022.10022118</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Edge Computing Methods for Environmental Monitoring IoT Sensors Using Neural Networks and Wavelet Transforms

Popis výsledku v původním jazyce

IoT sensor solutions have become ubiquitous in recent years. This rapid growth has resulted in large amounts of real-time data being transmitted to a cloud. Cloud computing paradigms have extensive requirements on data rates in a communications channel. The current trend is to use edge computing techniques to process data files primarily on the edge of a network. The present paper compares neural networks and wavelet transforms which apply compression methods to environmental parameter datasets. Three selected parameters (temperature, air pressure and wind speed) were compressed at ratios of 1:2, 1:4, 1:8 and 1:12. In addition, three different types of wavelet were compared in the wavelet transform. The results showed that the best RMSE compression result can be achieved by using a Biorthogonal wavelet, which compressed up to 93 % of the volume of data. In using a neural network to compress data, much depends on the nature of the data and also the amount of training data. With appropriately selected data, a volume reduction of 88 % and value of RMSE 0.5356 can be achieved.

Název v anglickém jazyce

Comparison of Edge Computing Methods for Environmental Monitoring IoT Sensors Using Neural Networks and Wavelet Transforms

Popis výsledku anglicky

IoT sensor solutions have become ubiquitous in recent years. This rapid growth has resulted in large amounts of real-time data being transmitted to a cloud. Cloud computing paradigms have extensive requirements on data rates in a communications channel. The current trend is to use edge computing techniques to process data files primarily on the edge of a network. The present paper compares neural networks and wavelet transforms which apply compression methods to environmental parameter datasets. Three selected parameters (temperature, air pressure and wind speed) were compressed at ratios of 1:2, 1:4, 1:8 and 1:12. In addition, three different types of wavelet were compared in the wavelet transform. The results showed that the best RMSE compression result can be achieved by using a Biorthogonal wavelet, which compressed up to 93 % of the volume of data. In using a neural network to compress data, much depends on the nature of the data and also the amount of training data. With appropriately selected data, a volume reduction of 88 % and value of RMSE 0.5356 can be achieved.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20202 - Communication engineering and systems

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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - proceedings

ISBN

978-1-66548-768-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

217-222

Název nakladatele

IEEE - Institute of Electrical and Electronics Engineers

Místo vydání

Piscataway

Místo konání akce

Singapur

Datum konání akce

4. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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