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Addendum published on 13 July 2015, see Sensors 2015, 15(7), 16831.

Open AccessArticle
Sensors 2015, 15(4), 9277-9304; doi:10.3390/s150409277

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

ESAI—Embedded Systems and Artificial Intelligence Group, Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/San Bartolomé, 46115 Valencia, Spain
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Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 25 February 2015 / Revised: 31 March 2015 / Accepted: 16 April 2015 / Published: 21 April 2015
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [4410 KB, uploaded 21 April 2015]   |  

Abstract

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. View Full-Text
Keywords: wireless sensor networks; artificial neural networks; on-line Back-Propagation; ambient intelligence; energy efficiency wireless sensor networks; artificial neural networks; on-line Back-Propagation; ambient intelligence; energy efficiency
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pardo, J.; Zamora-Martínez, F.; Botella-Rocamora, P. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes. Sensors 2015, 15, 9277-9304.

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