  We propose a probabilistic packet reception model for Bluetooth Low Energy
(BLE) packets in indoor spaces and we validate the model by using it for indoor
localization. We expect indoor localization to play an important role in indoor
public spaces in the future. We model the probability of reception of a packet
as a generalized quadratic function of distance, beacon power and advertising
frequency. Then, we use a Bayesian formulation to determine the coefficients of
the packet loss model using empirical observations from our testbed. We develop
a new sequential Monte-Carlo algorithm that uses our packet count model. The
algorithm is general enough to accommodate different spatial configurations. We
have good indoor localization experiments: our approach has an average error of
~1.2m, 53% lower than the baseline range-free Monte-Carlo localization
algorithm.
