Skip to main content
zenodoopen

ESP32C3 WiFi FTM RSSI Indoor Localization

<div> <div> <div> <h2><strong>Wi-Fi FTM RSSI Localization dataset</strong></h2> <p>Wi-Fi Fine Time Measurement for positioning / Indoor Localization in <strong>3 different locations</strong> and using <strong>8 different APs</strong> <br>&nbsp;<br>Custom APs using <strong>ESP32C3</strong> and Raw FTM is measured in nanoseconds <br>&nbsp;<br>Data is only measured at the Router Side <br>&nbsp;<br>Data is not measured at client side <br>&nbsp;<br>Has 4 datasets inside the zip folder with over <strong>100,000 data points</strong> <br>&nbsp;<br>Contains processed Wi-Fi FTM packets from various routers in:&nbsp; &nbsp;<br>1. University of Victoria, Engineering Office Wing (EOW) 3rd Floor <br>2. University of Victoria, Engineering Office Wing (EOW) 5th Floor <br>3. University of Victoria, Engineering and Computer Science (ECS) 1st Floor <br>&nbsp;<br>Each folder contains a training dataset and a testing dataset that is independent in time and space <br>&nbsp;<br>Router Time is synchronized using chrony</p> </div> </div> </div> <div> <div>Instructions:&nbsp;</div> <div> <div> <h2><strong>Dataset is in CSV format</strong></h2> <p>Relative Time (seconds) | X Position (meters) | Y Position (meters) | Feature 1 | Feature 2 | Feature 3 ..... <br>&nbsp; <br>Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera <br>&nbsp; <br>Map is in ROS2 PGM format that can read by ROS2 programs <br>&nbsp;<br>Data for the paper <br>&nbsp; <br>Wi-Fi and Bluetooth Contact Tracing Without User Intervention</p> <p><br><a href="https://ieeexplore.ieee.org/document/9866766" rel="nofollow">https://ieeexplore.ieee.org/document/9866766</a></p> <p>Please Cite As</p> <pre><code>@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} }</code></pre> </div> </div> </div>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

ScienceDex for faculty

Your academic CV and NIH biosketch

Make your CV in your school's format and your NIH biosketch from it, then download both as Word files.