  RF fingerprinting leverages circuit-level variability of transmitters to
identify them using signals they send. Signals used for identification are
impacted by a wireless channel and receiver circuitry, creating additional
impairments that can confuse transmitter identification. Eliminating these
impairments or just evaluating them, requires data captured over a prolonged
period of time, using many spatially separated transmitters and receivers. In
this paper, we present WiSig; a large scale WiFi dataset containing 10 million
packets captured from 174 off-the-shelf WiFi transmitters and 41 USRP receivers
over 4 captures spanning a month. WiSig is publicly available, not just as raw
captures, but as conveniently pre-processed subsets of limited size, along with
the scripts and examples. A preliminary evaluation performed using WiSig shows
that changing receivers, or using signals captured on a different day can
significantly degrade a trained classifier's performance. While capturing data
over more days or more receivers limits the degradation, it is not always
feasible and novel data-driven approaches are needed. WiSig provides the data
to develop and evaluate these approaches towards channel and receiver agnostic
transmitter fingerprinting.
