Indoor microlocation with BLE beacons and incremental rule learning

Location is one of the most valuable and extensively used information in mobile context-aware systems. Its understanding may vary from geolocation that uses GPS infrastructure to locate objects on Earth, up to microlocation, which aims at locating users and objects inside closed areas. Although geolocation can be considered as a mature field, there is an ongoing research in the area of microlocation. Despite that, microlocation techniques do not offer satisfactory level of accuracy and implementation flexibility to be practically incorporated into commercial solutions. This is mainly because of high workload that needs to be done in terms of maps preparation and algorithms tuning. In this paper we present a method that can overcome this issue by providing incremental rule learning algorithm for automated discovery of user location on a room-level accuracy. We also show a method of augmenting semantic annotations on physical objects with a use of Bluetooth Low Energy beacons.