Indoor self-localization via bluetooth low energy beacons

Indoor localization is concerned with mapping sensory data to physical locations inside buildings. Location of a user or a mobile device is an essential part of the context, and is therefore very useful for pervasive computing applications. Many proposals exist for solving the localization problem, typically based on image or radio signal processing, though the problem is still generally considered to be open,especially when costs and privacy constraints play an important role. In this paper, wepropose a solution based on the emerging Bluetooth Low Energy (BLE) standard andoff-the-shelf hardware. Such approach proves to satisfy economic constraints, whilechallenging in terms of accurate location. To translate beacon signals into locations,we consider several approaches, i.e., cosine similarity, nearest neighbourhoodclassification, and the nearest beacon. Our experiments indicate a vector basedapproach as the most suited one. In fact, we show its effectiveness in an actual officedeployment consisting of five indoor areas: three multiuser offices, a social corner,and a hallway. We achieve 90% and 80% for accuracy and F-measure, respectively