  In this paper, we present Hapi, a novel system that uses off-the-shelf
standard WiFi to provide pseudo-3D indoor localization. It estimates the user's
floor and her 2D location on that floor. Hapi is calibration-free, only
requiring the building's floorplans and its WiFi APs' installation location for
deployment. Our analysis shows that while a user can hear APs from nearby
floors as well as her floor, she will typically only receive signals from
spatially closer APs in distant floors, as compared to APs in her floor. This
is due to signal attenuation by floors/ceilings along with the 3D distance
between the APs and the user. Hapi leverages this observation to achieve
accurate and robust location estimates. A deep-learning based method is
proposed to identify the user's floor. Then, the identified floor along with
the user's visible APs from all floors are used to estimate her 2D location
through a novel RSS-Rank Gaussian-based method. Additionally, we present a
regression based method to predict Hapi's location estimates' quality and
employ it within a Kalman Filter to further refine the accuracy. Our evaluation
results, from deployment on various android devices over 6 months with 13
subjects in 5 different up to 9 floors multistory buildings, show that Hapi can
identify the user's exact floor up to 95.2% of the time and her 2D location
with a median accuracy of 3.5m, achieving 52.1% and 76.0% improvement over
related calibration-free state-of-the-art systems respectively.
