  The interconnectedness of all things is continuously expanding which has
allowed every individual to increase their level of interaction with their
surroundings. Internet of Things (IoT) devices are used in a plethora of
context-aware application such as Proximity-Based Services (PBS), and
Location-Based Services (LBS). For these systems to perform, it is essential to
have reliable hardware and predict a user's position in the area with high
accuracy in order to differentiate between individuals in a small area. A
variety of wireless solutions that utilize Received Signal Strength Indicators
(RSSI) have been proposed to provide PBS and LBS for indoor environments,
though each solution presents its own drawbacks. In this work, Bluetooth Low
Energy (BLE) beacons are examined in terms of their accuracy in proximity
estimation. Specifically, a mobile application is developed along with three
Bayesian filtering techniques to improve the BLE beacon proximity estimation
accuracy. This includes a Kalman filter, a particle filter, and a
Non-parametric Information (NI) filter. Since the RSSI is heavily influenced by
the environment, experiments were conducted to examine the performance of
beacons from three popular vendors in two different environments. The error is
compared in terms of Mean Absolute Error (MAE) and Root Mean Squared Error
(RMSE). According to the experimental results, Bayesian filters can improve
proximity estimation accuracy up to 30 % in comparison with traditional
filtering, when the beacon and the receiver are within 3 m.
