Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

نویسندگان

چکیده

Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, state-of-the-art system two radios proposes set novel machine learning-based subchannel classification techniques for adaptive (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on accuracy BlueEar’s AFH prediction. in paper, we revisit sensing-based classifier, one BlueEar. At first, build an independent implementation classifier Ubertooth radio. Using implementation, conduct experiment several learning classifiers where features are utilized. Our show that higher can be achieved choosing appropriate training actual maps.Our maps.

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ژورنال

عنوان ژورنال: Energies

سال: 2021

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en14040928