FIND is an open source indoor locating system for home automation, indoor local positioning and passive tracking. It uses your smartphone or laptop to pinpoint your position in your home or office with a location precision of below 10 sq ft.
FIND uses scanning of WiFi and Bluetooth:
FIND compiles these different signals can be compiled into a fingerprint which can be used to uniquely classify the current location of that device
A problem is that some environment-related factors change over time, such as changes in hardware/furniture, the presence of people and ambient humidity conditions meaning that fingerprinting isn’t a one-off activity.
The researchers conclude that the highest transmission power (+4 dBm) produces the best location accuracy. However, this uses a lot of battery power. Use of the lowest power (−20 dBm) only worsened the accuracy by 11.8%. Similarly, lowering the density of the beacons by around 50%, the error increase was only about 9.2%. Increasing the advertising interval didn’t have a significant impact on the accuracy.
The affect of beacon orientation was assessed and vertical orientation was found to be best. Read our previous article on orientation. As expected, introducing a slight electrical noise during the positioning phase did not significantly affect accuracy.
The paper explores indoor location algorithms and implements a fingerprinting system using RSSI that achieves an average error of 0.43m.
The authors’ ‘fuzzy logic type-2’ system allows for complex environments such as buildings with glass/metal corridors. They comment that fingerprinting requires pre-configuration which is one of the main disadvantages of this method.
The main insight is that along with the expected difference in the RSSI attenuation there is a considerable difference in the BLE signal variation at all transmission power levels with respect to distance. The variation increases and the localisation accuracy decreases from high to low transmission power levels:
Another observation is that outliers in the data tend to affect the localisation accuracy. Applying filters to the data, they achieved a location accuracy of 2.2 meters with a precision of 95%.
One comment we have is that the researchers didn’t try different beacons. As we mentioned in 2016, the RSSI stability also varies across different beacon models.
EgiGeoZone Geofence is a useful app for Android with over 10,000 users that allows you set up triggering based on location. There’s also a Bluetooth version that allows triggering in the vincinity of iBeacons.
The app is also open source on GitHub. Note that the app doesn’t yet work with the Android 8.0 background changes. The author is hoping someone else will fork the code and keep the app alive.
They have observed that the stability of the received Bluetooth signal strength RSSI depends on which Channel 37, 38 or 39 the signal is being received on. This is because the channels slightly overlap the WiFi channels and there can be other Bluetooth devices also using the same channels.
The method analyses the channels over time and chooses those it thinks has least interference and most stable RSSI. This reduces the positioning error by 0.2m, to 2.2m, at a distance of 3.6m.
Although the implementation is similar to SensorMesh™ and BeaconRTLS™ used together, their solution uses a proprietary mesh implementation and a proprietary data protocol. Consequently, their implementation suffers longer response time when used over longer physical distances. Their maximum inter-hop distance of 8 to 10 m also isn’t good due to non-optimal devices and non-optimal device positioning.
Manage beacons, buildings, zones and broadcast messages. The web interface shows staff activity and allows staff to be assigned to tasks. Staff can update task status and provide notes from their smartphones.