  Trusted identification is critical to secure IoT devices. However, the
limited memory and computation power of low-end IoT devices prevent the direct
usage of conventional identification systems. RF fingerprinting is a promising
technique to identify low-end IoT devices since it only requires the RF signals
that most IoT devices can produce for communication. However, most existing RF
fingerprinting systems are data-dependent and/or not robust to impacts from
wireless channels. To address the above problems, we propose to exploit the
mathematical expression of the physical-layer process, regarded as a function
$\mathbf{\mathcal{F}(\cdot)}$, for device identification.
$\mathbf{\mathcal{F}(\cdot)}$ is not directly derivable, so we further propose
a model to learn it and employ this function model as the device fingerprint in
our system, namely $\mathcal{F}$ID. Our proposed function model characterizes
the unique physical-layer process of a device that is independent of the
transmitted data, and hence, our system $\mathcal{F}$ID is data-independent and
thus resilient against signal replay attacks. Modeling and further separating
channel effects from the function model makes $\mathcal{F}$ID channel-robust.
We evaluate $\mathcal{F}$ID on thousands of random signal packets from $33$
different devices in different environments and scenarios, and the overall
identification accuracy is over $99\%$.
