The constraint of energy consumption is a serious problem in wireless sensor networks to which many solutions have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a nonlinear autoregressive network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviates the need for sensed data during sensors’ idle periods and saves over 65 percent of energy.

Conserving Energy Through Neural Prediction of Sensed Data / Aram, Siamak; Khosa, Ikramullah; Pasero, Eros Gian Alessandro. - In: JOURNAL OF WIRELESS MOBILE NETWORKS, UBIQUITOUS COMPUTING AND DEPENDABLE APPLICATIONS. - ISSN 2093-5374. - (2014).

Conserving Energy Through Neural Prediction of Sensed Data

ARAM, SIAMAK;KHOSA, IKRAMULLAH;PASERO, Eros Gian Alessandro
2014

Abstract

The constraint of energy consumption is a serious problem in wireless sensor networks to which many solutions have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a nonlinear autoregressive network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviates the need for sensed data during sensors’ idle periods and saves over 65 percent of energy.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2578137
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