Datasets:
Tasks:
Tabular Classification
Sub-tasks:
tabular-single-column-regression
Size Categories:
100K<n<1M
Language Creators:
expert-generated
License:
| license: cc-by-nc-sa-4.0 | |
| language_creators: | |
| - expert-generated | |
| pretty_name: WiFi RSSI Indoor Localization | |
| size_categories: | |
| - 100K<n<1M | |
| task_categories: | |
| - tabular-classification | |
| task_ids: | |
| - tabular-single-column-regression | |
| tags: | |
| - wifi | |
| - indoor-positioning | |
| - indoor-localisation | |
| - wifi-rssi | |
| - rssi | |
| - recurrent-neural-networks | |
| # WIFI RSSI Indoor Positioning Dataset | |
| 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. | |
| # Data Format | |
| X Position (m), Y Position (m), RSSI Feature 1 (dBm), RSSI Feature 2 (dBm), RSSI Feature 3 (dBm), RSSI Feature 4 (dBm), ... | |