Abstract:
Indoor positioning by wireless means is of significant
interest in a wide range of applications; Wi-Fi and Bluetooth Low
Energy (BLE) are popular candidate wireless technologies for
this. We present a novel BLE beacon placement algorithm that
complements existingWi-Fi infrastructure in a hybridWi-Fi/BLE
positioning system. Composite signal strength measurements thus
obtained, are used to derive machine learning models for location
estimation. Our results show that positioning estimates at tablelevel
granularity (0.9 m 1.8 m) in a computer laboratory can be
achieved with 97% accuracy. This is an improvement of 20% and
30% compared to Wi-Fi and BLE only techniques respectively.
