Evaluation of artificial neural networks for indoor positioning using Bluetooth Beacons

Indoor positioning opens up opportunities for a wide range of applications, including active marketing, accessibility and security. Although GPS (Global Positioning System) is widely used for outdoor location, it is inaccurate and in some cases unavailable indoors. One of the solutions is to use Bluetooth Beacons to determine the distance between the device and the beacon indoors using the Received Signal Strength Indicator (RSSI). The location of the object in the environment can be determined using at least three beacons and methods such as trilateration. This work aims to evaluate the use of artificial neural networks (ANN) to determine the distance and location of the laptop in an indoor environment. A first experiment compares the Log Distance Path Loss (LDPL) model and the ANN to determine the distance between the beacon and a laptop. A second experiment compares which method is best to determine the position of the laptop in a room. The following methods were evaluated: a) trilateration with distance calculation using the LDPL method; b) trilateration with distance calculation using an ANN; and c) position determination using an ANN. The results show that RSSI values can vary due to obstacles and the position of the antenna between the beacon and the laptop.