An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and batter...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 21; H. 12; S. 4153
Hauptverfasser:	Signoretti, Gabriel, Silva, Marianne, Andrade, Pedro, Silva, Ivanovitch, Sisinni, Emiliano, Ferrari, Paolo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 17.06.2021 MDPI
Schlagworte:	algorithm Algorithms Battery powered devices comparative effectiveness Compression algorithms Cost control Data analysis Data compression Data compression algorithms discrete cosine transform Discrete Cosine Transform(DCT) Discrete cosine transforms Eccentricity Evolving algorithm High-density applications Image coding Internet of thing (IOT) Internet of Things Low power electronics LPWAN Machine learning Online data compression Peak signal to noise ratio Sensors signal noise ratio Signal to noise ratio TinyML Wide area networks Wireless communications
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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