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

Result code in 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>
Alternative codes found
RIV/61989100:27740/22:10251709 RIV/61989100:27240/22:10251709
Result on the web
<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>

Result language
angličtina
Original language name
Comparison of Edge Computing Methods for Environmental Monitoring IoT Sensors Using Neural Networks and Wavelet Transforms
Original language description
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.
Czech name
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Czech description
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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

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - proceedings
ISBN
978-1-66548-768-9
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
217-222
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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