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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. KEYWORDSInternet of Things, Indoor Localization, Bluetooth Low Energy, Probabilistic packet reception model 2 RELATED WORK Now we discuss prior work related to wireless propagation models and indoor localization. Propagation models deal with loss in energy of radio waves between sender and receiver. Localization models track mobile nodes in an environment using seed nodes with known locations.

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