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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Conference papers
PIMRC 2014 - 25th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, Sep 2014, Washington, United States. 2014, Proceedings of PIMRC 2014. 〈http://www.ieee-pimrc.org/2014/〉
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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. 2014, Proceedings of PIMRC 2014. 〈http://www.ieee-pimrc.org/2014/〉. 〈hal-01094472〉

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