Bluetooth Low Energy Based Indoor Positioning on iOS Platform

Abstract

In this age of IoT (Internet of Things), Indoor Positioning (IPS) is considered as one of the most popular topics and has been researched widely all around the world, as the result of various applications it can provide. However, IPS is also a challenging topic that has a number of stringent requirements, such as cost, energy efficiency, availability and accuracy. The development of Bluetooth Low Energy (BLE) iBeacon has opened great opportunities for researchers to solve those challenges. In this paper, we present our iBeacon based positioning system, which we built as an application running on iOS platform. We also present Fingerprinting - the main positioning technique used in our system, in which we configure its fingerprints to improve accuracy. With that, a machine learning algorithm called k-Nearest Neighbor (kNN) is applied to extract the most probable user location. In addition, we also use Kalman Filter in order to enhance the stability of iBeacon's signal. Our system results in a 60% - 71.4% accuracy rate and an error of up to 1.6 m, which is acceptable in IPS.