  Accurate estimation of the confidence of an indoor localization system is
crucial for a number of applications including crowd-sensing applications,
map-matching services, and probabilistic location fusion techniques; all of
which lead to an enhanced user experience. Current approaches for quantifying
the output accuracy of a localization system in real-time either do not provide
a distance metric, require an extensive training process, and/or are tailored
to a specific localization system. In this paper, we present the design,
implementation, and evaluation of CONE: a novel calibration-free accurate
confidence estimation system that can work in real-time with any location
determination system. CONE builds on a sound theoretical model that allows it
to trade the required user confidence with tight bound on the estimated
confidence radius. We also introduce a new metric for evaluating confidence
estimation systems that can capture new aspects of their performance.
Evaluation of CONE on Android phones in a typical testbed using the iBeacons
BLE technology with a side-by-side comparison with traditional confidence
estimation techniques shows that CONE can achieve a consistent median absolute
error difference accuracy of less than 2.7m while estimating the user position
more than 80% of the time within the confidence circle. This is significantly
better than the state-of-the-art confidence estimation systems that are
tailored to the specific localization system in use. Moreover, CONE does not
require any calibration and therefore provides a scalable and ubiquitous
confidence estimation system for pervasive applications.
