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Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
- Source :
- Sensors (Basel, Switzerland), Sensors, Vol 21, Iss 7375, p 7375 (2021), Repositório Institucional da UFRGS, Universidade Federal do Rio Grande do Sul (UFRGS), instacron:UFRGS, Sensors, Volume 21, Issue 21
- Publication Year :
- 2021
- Publisher :
- MDPI, 2021.
-
Abstract
- 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.
- Subjects :
- Computer science
Real-time computing
Redes sem fio
forecasting
TP1-1185
Biochemistry
Article
Analytical Chemistry
Reduction (complexity)
Base station
energy saving
Energy saving
Electrical and Electronic Engineering
Representation (mathematics)
wireless sensor networks
Instrumentation
Redes neurais
Artificial neural network
business.industry
Deep learning
Chemical technology
Modelos de previsão
Energy consumption
Aprendizado de máquina
neural networks
Atomic and Molecular Physics, and Optics
Wireless sensor networks
Transmission (telecommunications)
Artificial intelligence
business
Wireless sensor network
Neural networks
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 21
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....19598a7c1743306f62060e3b5bf78bb9