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Communication Dans Un Congrès Année : 2021

Edge Computing Technique for a 87% Energy Saving for IoT Device Dedicated to Environmental Monitoring

Résumé

In this paper, we propose a method based on polynomial regression to perform data compression at the edge in order to reduce the energy consumption of connected objects. Our study is based on the trade-off between data compression at the edge and minimizing the error on data reconstruction on the server. A polynomial regression was experienced on a two-year database of temperature measurements. The error percentage between our data compression and the real temperature evolution have been assessed to determine the most relevant polynomial regression. A digital implementation of the data compression on an Arduino was carried out with a set of data collected over three weeks of experiments. The analyzis of the results concluded about the efficiency of our method with a reduction of 87% energy with an acceptable accuracy of 0.2◦C for temperature data collection.

Domaines

Electronique
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Dates et versions

hal-03042264 , version 1 (06-12-2020)

Identifiants

  • HAL Id : hal-03042264 , version 1

Citer

Francois Rivet, Laura Foucaud, Guillaume Ferré. Edge Computing Technique for a 87% Energy Saving for IoT Device Dedicated to Environmental Monitoring. 12th IEEE Latin American Symposium on Circuits and Systems (LASCAS), Feb 2021, Arequipa, Peru. ⟨hal-03042264⟩
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