  Many infrastructure-free indoor positioning systems rely on fine-grained
location-dependent fingerprints to train models for localization. The site
survey process to collect fingerprints is laborious and is considered one of
the major obstacles to deploying such systems. In this paper, we propose TuRF,
a fast path-based fingerprint collection mechanism for site survey. We
demonstrate the feasibility to collect fingerprints for indoor localization
during walking along predefined paths. A step counter is utilized to
accommodate the variations in walking speed. Approximate location labels
inferred from the steps are then used to train a Gaussian Process regression
model. Extensive experiments show that TuRF can significantly reduce the
required time for site survey, without compromising the localization
performance.
