Optimization of BLE Beacon Density for RSSI-Based Indoor Localization

Location Based Services (LBS) and Proximity Based Services (PBS) can play an important role in our daily life by simplifying tasks. Functions such as turning on and off lights can occur automatically or locking and unlocking doors can be done using LBS. By knowing the location of a user, appliances can be automated to function once the user is near them. Through the use of indoor localization, a user's position can be calculated. When designing an indoor localization system the density of transmitters plays an important role in maximizing the accuracy obtained. Increasing the number of references can improve the accuracy by providing additional information that the system can use in calculating a location. However, placing too many transmitters in the area can create interference in signals and negatively impact the localization results, while not having enough transmitters will hinder localization as not enough information is available. In this paper, we examine the optimal number of Bluetooth Low Energy (BLE) beacons to be used for indoor localization to optimize localization accuracy. Two algorithms were compared: trilateration and nonlinear least squares applying two types of filtering: moving average, and Kalman. Nine different types of systems were developed and compared in terms of accuracy and precision. According to experimental results placing six beacons in an environment will produce the optimal results. Using a nonlinear least squares algorithm with the three closest references with a moving average filter produced the lowest error of 1.149 meters with a standard deviation of 0.698 meters.