Revisiting Bluetooth Adaptive Frequency Hopping Prediction with a Ubertooth

This article presents a customized approach for training a supervised learning neural network with the adaptive moment estimation algorithm, to classify the number of frequency hopping networks in an operational area. The algorithm was constructed based on data experimentally collected from a real-time spectrum analyzer for military very high frequency hopping networks. The impact of some training parameters on classification efficiency is briefly discussed while the obtained accuracy was above 97% for both test and validation data in all training variations. With this promising result, the proposed algorithm has the potential to be utilized in developing operational systems capable of real-time signal reconnaissance for military frequency hopping radio networks.

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

Lee

Park

Roh

2021

Energies

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

show abstract

Revisiting Bluetooth Adaptive Frequency Hopping Prediction with a Ubertooth

Cited by 3 publications

References 10 publications

Analysis on Attributes of Software Defined Radio for Space Communication

Analysis on Attributes of Software Defined Radio for Space Communication

Machine-Learning-Based Classification of Frequency Hopping in Radio Networks for Communication Reconnaissance

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

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