Experiences with using iBeacons for Indoor Positioning
Deepesh 2016 This paper is an analysis of the realities of implementing Bluetooth Low Energy iBeacons (Apple) in a confined space for indoor positioning. It examines two techniques, trilateration and fingerprinting, based on the RSSI of the Bluetooth Beacons. The researchers built a custom app on an Apple iPad 2 to perform the experiments and check their positioning methods against the device's actual position.
The laboratory setup was a rectangular room divided into 18 sections, 6 across and 3 down. iBeacons were placed in the corners of the room and all fixed to the same height. There were three pillars in the room that created obstacles for the BLE signals.
Trilateration was first used, but abandoned quickly as the researchers found that there was large variations in the RSSI of the iBeacons. The variations made triangulating the position very challenging and no data was obtained.
Fingerprinting was used next and data was gathered. Offline training data was collected by the device and then fed into two different machine learning algorithms. The first was a k Nearest Neighbor algorithm. The researchers found that with this algorithm, the location of the device was placed within the correct section of the room 62.7% of the time. This indicates that there was an average of 2-3 meters shift in radius from the actual position of the device.
The second algorithm tested is the Random Forest Algorithm, using the average solution from hundreds of decision trees to position the device. This solution was able to achieve a correct section position 79% of the time.
Once the tests were concluded, the researchers found a few key insights to indoor positioning. There are vast variations in the RSSI readings of BLE signals, making the error of the positioning algorithms tested relatively high. The device moving around, other people near the device, fixed obstacles near the device, the presence of other devices near the device, and orientation of the device and iBeacons make large, unpredictable changes to the RSSI. These variances create a huge challenge for implementing BLE indoor positioning in crowded and dynamic environments like shopping malls and airports.