iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
It’s often the case you need to know if a beacon is working and advertising the correct information. It’s also sometimes necessary to differentiate between beacons, based on their signal strength, so you know you are setting up the correct beacon. Other times, you might want to know a beacon’s MAC address. The best scanning … Continue reading “Testing if a Beacon is Working”
A problem with navigation in vehicles is that location can be lost in radio-shadows such as in tunnels and in tree covered areas. ChoonSung Nam and Dong-Ryeol Shin of Sungkyunkwan University, Suwon, Korea have a new paper on Vehicle location measurement method for radio-shadow area through iBeacon. Beacons are placed at the side of the … Continue reading “Enhanced Vehicle GPS Using Beacons”
In our previous article iBeacon Microlocation Accuracy, we wrote about ways of using beacon RSSI to determine location. However, what if you were to use and combine beacon RSSI with other ways of locating to create a hybrid method? This is the topic of a new research Unsupervised Indoor Localization Based on Smartphone Sensors, iBeacon and … Continue reading “Hybrid Localisation Method”
Customers often ask us the accuracy when locating beacons. In order to get the answer, its necessary to understand different ways of locating and the tradeoffs that are needed to get the different levels of accuracy. There are two types of locating, received signal strength (RSSI) based and angle of arrival direction finding (AoA). Locating … Continue reading “iBeacon Microlocation Accuracy”
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”