A scalable and computationally efficient framework is designed to fingerprint
real-world Bluetooth devices. We propose an embedding-assisted attentional
framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its
generalization capability is analyzed in different settings and the effect of
sample length and anti-aliasing decimation is demonstrated. The embedding
module serves as a dimensionality reduction unit that maps the high dimensional
3D input tensor to a 1D feature vector for further processing by the ATN
module. Furthermore, unlike the prior research in this field, we closely
evaluate the complexity of the model and test its fingerprinting capability
with real-world Bluetooth dataset collected under a different time frame and
experimental setting while being trained on another. Our study reveals 7.3x and
65.2x lesser memory usage with Mbed-ATN architecture in contrast to Oracle at
input sample lengths of M=10 kS and M=100 kS respectively. Further, the
proposed Mbed-ATN showcases 16.9X fewer FLOPs and 7.5x lesser trainable
parameters when compared to Oracle. Finally, we show that when subject to
anti-aliasing decimation and at greater input sample lengths of 1 MS, the
proposed Mbed-ATN framework results in a 5.32x higher TPR, 37.9% fewer false
alarms, and 6.74x higher accuracy under the challenging real-world setting.

设计了一种可伸缩和计算效率高的嵌入式注意力框架 (Mbed-ATN)，该方案可用于指纹识别实际蓝牙设备。