Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN

One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this probl...

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Autores principales:	Carlos R. Morales, Fernando Rangel de Sousa, Valner Brusamarello, Nestor C. Fernandes
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wireless sensor networks forecasting energy saving neural networks Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/af695a3400ba4e4786aa9ba5820e75fa
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spelling	oai:doaj.org-article:af695a3400ba4e4786aa9ba5820e75fa2021-11-11T19:18:40ZEvaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN10.3390/s212173751424-8220https://doaj.org/article/af695a3400ba4e4786aa9ba5820e75fa2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7375https://doaj.org/toc/1424-8220One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.Carlos R. MoralesFernando Rangel de SousaValner BrusamarelloNestor C. FernandesMDPI AGarticlewireless sensor networksforecastingenergy savingneural networksChemical technologyTP1-1185ENSensors, Vol 21, Iss 7375, p 7375 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wireless sensor networks forecasting energy saving neural networks Chemical technology TP1-1185
spellingShingle	wireless sensor networks forecasting energy saving neural networks Chemical technology TP1-1185 Carlos R. Morales Fernando Rangel de Sousa Valner Brusamarello Nestor C. Fernandes Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
description	One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.
format	article
author	Carlos R. Morales Fernando Rangel de Sousa Valner Brusamarello Nestor C. Fernandes
author_facet	Carlos R. Morales Fernando Rangel de Sousa Valner Brusamarello Nestor C. Fernandes
author_sort	Carlos R. Morales
title	Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
title_short	Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
title_full	Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
title_fullStr	Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
title_full_unstemmed	Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
title_sort	evaluation of deep learning methods in a dual prediction scheme to reduce transmission data in a wsn
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/af695a3400ba4e4786aa9ba5820e75fa
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Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN

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