Modeling and simulation of user movements with bio-inspired computing.
Mobile applications for sensing and collection data.
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.
|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.
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
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.
Explore deep statistics about device collected data. Useful as start for other tools, when you discover dates and times of sensing of device.