  A novel cross-domain attentional multi-task architecture - xDom - for robust
real-world wireless radio frequency (RF) fingerprinting is presented in this
work. To the best of our knowledge, this is the first time such comprehensive
attention mechanism is applied to solve RF fingerprinting problem. In this
paper, we resort to real-world IoT WiFi and Bluetooth (BT) emissions (instead
of synthetic waveform generation) in a rich multipath and unavoidable
interference environment in an indoor experimental testbed. We show the impact
of the time-frame of capture by including waveforms collected over a span of
months and demonstrate the same time-frame and multiple time-frame
fingerprinting evaluations. The effectiveness of resorting to a multi-task
architecture is also experimentally proven by conducting single-task and
multi-task model analyses. Finally, we demonstrate the significant gain in
performance achieved with the proposed xDom architecture by benchmarking
against a well-known state-of-the-art model for fingerprinting. Specifically,
we report performance improvements by up to 59.3% and 4.91x under single-task
WiFi and BT fingerprinting respectively, and up to 50.5% increase in
fingerprinting accuracy under the multi-task setting.
