Data Collection: Proximityness

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Clearance for public release of dataset has been granted

Introduction

We conducted a study with 126 subjects, over three months, collecting data from various sensors, that resulted in a multimodal dataset for co-presence detection.

The collected study data from mobile devices:

Data characteristic	Sampling rate	Study data
User activity	5 s	(Linear) accelerometer
User position	5 min	GPS, network
User environment	5 min	Barometer, magnetometer, temperature, light, gravity, gyroscope, rotation, GSM towers, Bluetooth and Wi-Fi devices

We publish a subset of the original data set in the period between 01.06.2018 and 15.06.2018 including Wi-Fi scans as proximity verification set, magnetometer as sensor data, the positions of Wi-Fi access points, and magnetometer's sensor hardware.

Data Description

Detailed description of the data is presented below:

proximity_verification_set_wifi.csv.gz contains the scans of Wi-Fi access points including mobile device's id, relative time, and BSSID.
We have anonymized the device id and BSSID with substitute identifier. In addition, we converted the absolute timestamp to a relative time using a time reference point.

proximity_verification_set_wifi_positions.csv.gz contains the positions of Wi-Fi access points including BBSID and relative position.
We have anonymized the BSSID and coordinates of Wi-Fi access points with substitute identifier.

sensor_data_magnetometer.csv.gz contains the magnetometer's sensor data including mobile device's id, relative time, and x, y, z of magnetometer sensor.
We have anonymized the device id with substitute identifier and converted the absolute timestamp to a relative time using a time reference point.

sensor_hardware_magnetometer.csv.gz contains the magnetometer's sensor hardware including mobile device's id and hardware of magnetometer sensor.
We have anonymized the device id and sensor hardware with substitute identifier.

How to Cite

M. Haus, A. Y. Ding, and J. Ott, "Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity," in Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2020, pp. 1-7.