ESP32C3 WiFi FTM RSSI Indoor Localization

Wi-Fi FTM RSSI Localization dataset

Wi-Fi Fine Time Measurement for positioning / Indoor Localization in 3 different locations and using 8 different APs
 
Custom APs using ESP32C3 and Raw FTM is measured in nanoseconds
 
Data is only measured at the Router Side
 
Data is not measured at client side
 
Has 4 datasets inside the zip folder with over 100,000 data points
 
Contains processed Wi-Fi FTM packets from various routers in:   
1. University of Victoria, Engineering Office Wing (EOW) 3rd Floor
2. University of Victoria, Engineering Office Wing (EOW) 5th Floor
3. University of Victoria, Engineering and Computer Science (ECS) 1st Floor
 
Each folder contains a training dataset and a testing dataset that is independent in time and space
 
Router Time is synchronized using chrony

Dataset is in CSV format

Relative Time (seconds) | X Position (meters) | Y Position (meters) | Feature 1 | Feature 2 | Feature 3 .....
 
Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera
 
Map is in ROS2 PGM format that can read by ROS2 programs
 
Data for the paper
 
Wi-Fi and Bluetooth Contact Tracing Without User Intervention

https://ieeexplore.ieee.org/document/9866766

Please Cite As

@article{yuen2022wi,
  title={Wi-Fi and Bluetooth contact tracing without user intervention},
  author={Yuen, Brosnan and Bie, Yifeng and Cairns, Duncan and Harper, Geoffrey and Xu, Jason and Chang, Charles and Dong, Xiaodai and Lu, Tao},
  journal={IEEE Access},
  volume={10},
  pages={91027--91044},
  year={2022},
  publisher={IEEE}
}