Unsupervised indoor localization based on Smartphone Sensors, iBeacon and Wi-Fi

In this paper, we proposed UILoc, an unsupervised indoor localization scheme that uses the combination of smartphone sensors, iBeacons and Wi-Fi fingerprints for reliable and accurate indoor localization with zero labor cost. Firstly, compared with fingerprint-based method, UILoc system can build the fingerprint database automatically without any site survey and the database will be applied in the fingerprint localization algorithm. Secondly, since the initial position is vital to the system, the UILoc will provide the basic location estimation through the PDR method. To provide accurate initial localization, this paper proposed an initial localization module, a weighted fusion algorithm combined KNN algorithm and Least squares algorithm. In UILoc, we also designed a reliable model to reduce the landmark correction error. The experimental results show that the UILoc can provide accurate positioning and the average localization error is about 1.1 meters in the steady state and the maximum error is 2.77 meters.