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Trusted identification is critical to secure IoT devices. However, thelimited memory and computation power of low-end IoT devices prevent the directusage of conventional identification systems. RF fingerprinting is a promisingtechnique to identify low-end IoT devices since it only requires the RF signalsthat most IoT devices can produce for communication. However, most existing RFfingerprinting systems are data-dependent and/or not robust to impacts fromwireless channels. To address the above problems, we propose to exploit themathematical 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 proposea model to learn it and employ this function model as the device fingerprint inour system, namely $\mathcal{F}$ID. Our proposed function model characterizesthe unique physical-layer process of a device that is independent of thetransmitted data, and hence, our system $\mathcal{F}$ID is data-independent andthus resilient against signal replay attacks. Modeling and further separatingchannel 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 overallidentification accuracy is over $99\%$.

FID: Function Modeling-based Data-Independent and Channel-Robust Physical-Layer Identification