  WiFi fingerprinting is one of the mainstream technologies for indoor
localization. However, it requires an initial calibration phase during which
the fingerprint database is built manually. This process is labour intensive
and needs to be repeated with any change in the environment. While a number of
systems have been introduced to reduce the calibration effort through RF
propagation models or crowdsourcing, these still have some limitations. Other
approaches use the recently developed iBeacon technology as an alternative to
WiFi for indoor localization. However, these beacon-based solutions are limited
to a small subset of high-end phones. In this paper, we present HybridLoc: an
accurate low-overhead indoor localization system. The basic idea HybridLoc
builds on is to leverage the sensors of high-end phones to enable localization
of lower-end phones. Specifically, the WiFi fingerprint is crowdsourced by
opportunistically collecting WiFi-scans labeled with location data obtained
from BLE-enabled high-end smart phones. These scans are used to automatically
construct the WiFi-fingerprint, that is used later to localize any lower-end
cell phone with the ubiquitous WiFi technology. HybridLoc also has provisions
for handling the inherent error in the estimated BLE locations used in
constructing the fingerprint as well as to handle practical deployment issues
including the noisy wireless environment, heterogeneous devices, among others.
Evaluation of HybridLoc using Android phones shows that it can provide accurate
localization in the same range as manual fingerprinting techniques under the
same conditions. Moreover, the localization accuracy on low-end phones
supporting only WiFi is comparable to that achieved with high-end phones
supporting BLE. This accuracy is achieved with no training overhead, is robust
to the different user devices, and is consistent under environment changes.
