Indoor Localization with lightweight RSS Fingerprint using BLE iBeacon on iOS platform

In the near future, location-based services (LBSs) will become an essential part of the smart city. That's why indoor localization has been attracting researcher attention. A popular method for indoor localization is Fingerprinting. However, it is not easy to reach high accuracy in positioning with only Fingerprinting. Several current techniques could be used to improve the accuracy of Fingerprinting such as Machine Learning (ML) or Deep Learning (DL). In this paper, we propose an BLE iBeacon-based indoor localization system using Fingerprinting method. The system applies k-nearest neighbors (k-NN) - an ML algorithm to decide the user position. In order to reduce the computational cost and ensure the accuracy of the system, we propose to use lightweight feature vectors that include information of the nearest beacons and device azimuth for training Machine Learning algorithm. We performed some experiments to verify the proposed system. The results show that the method provides a feasible indoor positioning solution with high accuracy.