10.21227/V0KY-0P46
Minh Tu Hoang
Minh Tu
Hoang
tuminhhoang@uvic.ca
Xiaodai Dong
Xiaodai
Dong
xdong@ece.uvic.ca
Tao Lu
Tao
Lu
taolu@ece.uvic.ca
Brosnan Yuen
Brosnan
Yuen
Robert Westendorp
Robert
Westendorp
WiFi RSSI Indoor Localization
IEEE DataPort
2019
Artificial Intelligence
IoT
Machine Learning
Received signal strength indicator (RSSI)
WiFi indoor localization
K-nearest neighbor (KNN)
fingerprint-based localization
2019-12-12
Open Access Dataset
Creative Commons Attribution
A reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.We use an autonomous robot to collect the WiFi fingerprint data. Our 3-wheel robot has multiple sensors including wheel odometer, an inertial measurement unit (IMU), a LIDAR, sonar sensors and a color and depth (RGB-D) camera. The robot can navigate to a target location to collect WiFi fingerprints automatically. The localization accuracy of the robot is 0.07 m ± 0.02 m. The dimension of the area is 21 m × 16 m. It has three long corridors. There are six APs and five of them provide two distinct MAC address for 2.4- and 5-GHz communications channels, respectively, except for one that only operates on 2.4-GHz frequency. There is one router can provide CSI information.