Beacon Location Accuracy

There’s some recent new research on ‘Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and Fingerprint Map’ by Damian Grzechca, Piotr Pelczar, Łukasz Chruszczyk.

They evaluated RSSI and indoor positioning trilateration algorithms in order to determine location accuracy. After lots of experimentation and mathematics, they calculated the average error to be 1.09m for 1–9m and 1.75m for 1-20m and after trilateration an average error 2.45m was achieved.

The conclusions give some hints how better results might be achieved. For example, correlating the RSSI with accelerometer, gyroscope and other sensors. Other strategies might be to excluding areas where an object
cannot move, or filtering out situations where objects move but accelerometer measurements don’t match.

Crowd Analysis Using Beacons

With so many uses of beacons centred around notifications to users, it’s interesting to see Queen Mary University of London doing something different. Research by Kleomenis Katevas, Laurissa Tokarchuk, Hamed Haddadi and Richard G. Clegg of the Department of Computing of Imperial College looks into detecting group (crowd) formations using iBeacon (pdf).

They used beacon RSSI and phone motion together with algorithms based on graph theory to predict interactions inside the crowd. They verified their finding using using video footage as ground truth.


The paper has some particularly interesting observations from testing RSSI in an EMC screened anechoic chamber and also has some information on distance estimation models.