iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
There’s a paper by Mariusz Kaczmarek, Jacek Ruminski and Adam Bujnowski of Gdansk University of Technology on the Accuracy analysis of the RSSI BLE SensorTag signal for indoor localization purposes (pdf). They studied the radio signal from multiple Texas Instruments SensorTag CC2650 devices in order to determine if it could be used to determine location. … Continue reading “Research Paper on Using Bluetooth for Indoor Locating”
The problem with using RSSI for detecting location is that raw data contains lots of noise. Also, this noise becomes more prevalent in the viewed data when location samples are taken less often. There’s a useful new article at InfoQ on Processing Streaming Human Trajectories with WSO2 CEP. The idea uses Kalman filtering to smooth … Continue reading “Beacon Trajectory Smoothing”
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 … Continue reading “Beacon Location Accuracy”
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 … Continue reading “Crowd Analysis Using Beacons”