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An efficient learning technique to predict link quality in WSN

Abstract : In this paper, we apply learning techniques to predict link quality evolution in a wireless sensor network (WSN) and take advantage of wireless links with the best possible quality to improve the packet delivery rate. We model this problem as a forecaster prediction game based on the advice of several experts. The forecaster learns on-line how to adjust its prediction to better fit the environment metric values. Simulations using traces collected in a real WSN show the improvement of the prediction when the experts use the SES prediction strategy, whereas the forecaster uses the EWA learning strategy.
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Contributor : Pascale Minet <>
Submitted on : Monday, February 23, 2015 - 5:19:08 PM
Last modification on : Friday, January 10, 2020 - 3:42:21 PM
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  • HAL Id : hal-01094472, version 1



Dana Marinca, Pascale Minet, Nesrine Ben Hassine. An efficient learning technique to predict link quality in WSN. PIMRC 2014 - 25th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, Sep 2014, Washington, United States. ⟨hal-01094472⟩



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