The Ontology Driven Indoor Positioning System Based On BLE Beacon Localization

Abstract

One of the problems associated with the implementation of indoor location detection systems is the time-consuming procedure of equipment adjustment, which includes indoor map construction, radio signal map creation and calibrating signal propagation model. Thus, the equipment adjustment is a time-consuming and expensive process to be perform every time when there are changes in equipment configuration and allocation. The developed indoor localization system provides navigation of the user inside a room and allows to building radio map and putting Bluetooth Low Energy (BLE) beacons on the map of a room by the efforts of a number of users walking indoors. The architecture of the system is developed so that the different indoor localization techniques can be used and different services can be requested by the users mobile application. The user's navigation inside the room is a combination of PDR based on the built-in smartphone sensors, multilateration and fingerprinting. The indoor navigation ontology is implemented to make decision which of these methods should be used. The key feature of the system is determining the location of BLE beacon. For this purpose the Random Forest algorithm is used, which uses signal levels, user rotation angles and distance to Bluetooth beacon as a training dataset. The geometric parameters of a room are estimated by the radio map and Bluetooth beacon locations.

Keywords

• indoor navigation
• ontology
• multilateration
• machine learning