  Device-free localization (DFL) is an emerging technology for estimating the
position of a human or object that is not equipped with any electronic tag, nor
participate actively in the localization process. Similar to device-based
localization, the initial phase in DFL is to build the fingerprint database
which is usually done manually using site surveying. This process is tedious,
time-consuming, and vulnerable to environmental dynamics. Motivated by the
recent advances in the Internet of Things (IoT), this paper introduces
RadioGrapher; a system that automates the process of device-free fingerprint
calibration in IoT environments. RadioGrapher leverages the device-based
locations of entities in the area of interest in a crowd-sensing manner, aided
with Fresnel zones of the wirelessly connected IoT devices to automatically
construct a device-free fingerprint. Experimental evaluation of RadioGrapher in
an IoT testbed using multiple entities shows that it can construct DFL
fingerprints with high accuracy. Moreover, its median localization accuracy is
comparable to that of manual fingerprinting. This comes with no calibration
overhead, highlighting the promise of RadioGrapher as a crowdsourcing
device-free fingerprint constructor in IoT environments.
