Implementing an iBeacon Indoor Positioning System using Ensemble Learning Algorithm

Nowadays, there is an increasing requirement for indoor positioning and navigation with Location Based Services (LBSs). Many applications on smartphones exploit different techniques and inputs for positioning. Most of the indoor wireless positioning systems rely on Received Signal Strengths (RSSs) from indoor wireless emitting devices. However, the accuracy of indoor position is easily affected by servals signal interference. In this paper, we propose a LBS system using ensemble machine learning with iBeacon RSSs fingerprint, and hope to achieve higher accuracy for indoor positioning. Preliminary experiments showed very promising results that our approach can improve indoor positioning accuracy at shorter distance.