BLE beacon indoor localization dataset

This dataset was created to facilitate research into indoor localization with BLE beacons. Data was collected from September 2018 to May 2019 in two separate locations. Several participants assisted with the experiment each carrying a BLE beacon and a smartphone. This dataset is released in hope that researchers will use it as a benchmark for indoor localization techniques. We initially collected this dataset noting a lack of available indoor localization datasets with labeled data. Our implementation uses the fixed-receiver moving-transmitter method of indoor localization. We use Raspberry Pis and BLE beacons as our receivers and transmitters respectively. Android Minimum Version | 4.4 Beacon Type | Gimbal Series 10 Beacon Profile | iBeacon Beacon Transmit Power | 0 dbm Beacon Transmission Interval | 10 Hz Beacon Calibrated Power | Unused Edge Type | Raspberry Pi 3 Raspberry Pi OS | Raspbian Jessie Lite Raspberry Pi Software | Beaconpi Edge Placement Height | 1.6 Metres We use Raspberry Pis and BLE beacons as our receivers and for brevity we subsequently use edge and beacon' to refer to these. The data recording was done via the Beaconpi software. Beaconpi runs both on the edges and a centralized server which communicates bidirectionally between the two main components. The edges and server components sync to the same clock to ensure all elements of the system have a consistent clock. The frame records the fingerprint and the Received Signal Strength Indicator (RSSI) at the edge. To validate our methods we also collect ground truths via an Android application (MapTracker) as the participants move around the experiment area. The Android application records the location of the wearer via self reporting and also collects the accelerometer and gyroscope data for the participant. The participants were asked to randomly walk around the room for around 3 minutes at least 3 times a day. This varied as is evident from the dataset which was expected. The participants walked in straight lines between landmarks that were placed frequently in the space. More details exist in the README.md file. The dataset can also be accessed in GitHub: BLE Beacon Indoor Localization Dataset (BBIL Dataset) [Copyright (c) 2019 Maeve Kennedy]