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Erschienen in:
2017 | OriginalPaper | Buchkapitel
DPCAS: Data Prediction with Cubic Adaptive Sampling for Wireless Sensor Networks
verfasst von : Leonardo C. Monteiro, Flavia C. Delicato, Luci Pirmez, Paulo F. Pires, Claudio Miceli
Erschienen in: Green, Pervasive, and Cloud Computing
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Abstract
The advance of the Wireless Sensor Network (WSN) technology opens many possibilities for several kinds of applications. This kind of network, though, presents as main limitation the lack of a permanent and reliable energy supply. Keep the energy supply of a WSN for long periods may constitute a significant obstacle for its implementation. There are many strategies to prolong the energy supply of a WSN, one of them, knows as Dual Prediction Scheme (DPS) is explored in this article. This work proposes a new DPS technique, called DPCAS (Dual Prediction with Cubic Adaptive Sampling), combining adaptive sampling with prediction models based in exponential time series. Simulations were carry on with the use of this new technique and the results were promising in quality of the generated data and energy save in the sensor nodes.