Indoor navigation systems based on iBeacon fingerprinting

Abstract

Thesis under the direction of Dr. Jules White This thesis investigates the use of iBeacon fingerprinting as a localization technique for indoor navigation systems. Fingerprinting uses machine learning to generate a signature for each location based on its Bluetooth signal characteristics. In this thesis, we examine key questions related to how machine learning parameters and beacon setup influence the performance of indoor navigation localization. Our empirical results show that Random Forest provides the best localization performance and can provide high accuracy localization with as few as two visible beacons per location.