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Improving BLE deterministic fingerprinting by using a weighted k-NN algorithm over filtered RSSI data

Fingerprinting systems are a simple and efficient way to build positioning systems for Location Based Services (LBS) applications in smartphones. The technologies that can be used for radio frequency fingerprinting with off-the-shelf smartphones are Wi-Fi, which has been deeply studied, and Bluetooth Low Energy (BLE), which is receiving more attention in the last years. This work presents an improvement in deterministic fingerprinting using a filtering process and a new function to compute the Weighted k Nearest Neighbours (Wk-NN) location algorithm. Results with a BLE data set show that these improvements reduce the localization error and give a simple way to build a fingerprinting positioning system for smartphones with an average precision of less than a meter.