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> <br>Custom APs using <strong>ESP32C3</strong> and Raw FTM is measured in nanoseconds <br> <br>Data is only measured at the Router Side <br> <br>Data is not measured at client side <br> <br>Has 4 datasets inside the zip folder with over <strong>100,000 data points</strong> <br> <br>Contains processed Wi-Fi FTM packets from various routers in: <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> <br>Each folder contains a training dataset and a testing dataset that is independent in time and space <br> <br>Router Time is synchronized using chrony</p> </div> </div> </div> <div> <div>Instructions: </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> <br>Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera <br> <br>Map is in ROS2 PGM format that can read by ROS2 programs <br> <br>Data for the paper <br> <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
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