ESP32C3 WiFi FTM RSSI Indoor Localization
- Citation Author(s):
-
Brosnan Yuen
Yifeng BieDuncan CairnsGeoffrey HarperJason XuXiaodai Dong
Tao Lu
- Submitted by:
- Brosnan Yuen
- Last updated:
- DOI:
- 10.21227/2mbb-yt66
- Data Format:
- Research Article Link:
- Links:
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Abstract
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
Mirror Download
https://zenodo.org/records/10883013
Instructions:
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} }
In reply to 2 by Carlos Quiroga
Hello