COhave research

Modeling and simulation of user movements with bio-inspired computing.
Mobile applications for sensing and collection data.

Download GPS/WiFi dataset See our publications

4+ years, 500+ devices,
50 000 000+ GPS, WiFi records

From our experiments we offer data set, that we collected with 455 mobile devices distributed among our students at university. Locations and WiFi networks are collected worldwide.

Year GPSv1 GPSv2 WiFi
2015 6 530 019 - -
2016 13 183 118 460 199 12 287 597
2017 441 786 463 671 13 425 877
2018 83 633 212 971 7 439 015
2019 - 54 403 1 622 007
2020 - 13 145 622 945

You can download report from WiFi dataset analysis written in Slovak language here.

Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

Modeling movement based on WiFi

We have proposed, implemented and compared several approaches for user movement (trajectory) extraction. Unlike other approaches, our approaches are purely based on WiFi sensing without the knowledge of user's physical location. This is a favorable approach in scenarios that aim at high energy efficiency. We only collect WiFi information passively, i.e. we only listen to broadcast beacons and do not transmit any probe requests.

Maroš Čavojský, Marek Uhlar, Marian Ivanis, Martin Molnar, Martin Drozda. User Trajectory Extraction Based on WiFi Scanning. 2018 6th International Conference on Future Internet of Things and Cloud Workshops, pp. 115-120, IEEE, 2018

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Trajectory recording using WiFi scanning

Mobile devices are equipped with a GPS receiver, but its frequent use can cause a high battery drain. Our design of continuous location tracking aims at providing a reasonable trade-off between energy consumption, location acquisition accuracy. We use WiFi scanning for decisions about when to start using GPS receiver. The experiments show that our design provides significant energy savings when compared to other continuous location tracking alternatives. Maroš Čavojský and Martin Drozda. Energy Efficient Trajectory Recording of Mobile Devices Using WiFi Scanning. The First International Workshop on Crowd Intelligence for Smart Cities. Proc. of 2016 Intl. IEEE Conferences on UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld Congress, pp. 1079-1085, IEEE, 2016.

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WiFi Places Markov Chain (BiGram) Try it

Explore device collected statistics

Explore deep statistics about device collected data. Useful as start for other tools, when you discover dates and times of sensing of device.

Explore statistics