Mobile User Location Tracking with Unreliable Data

Abstract

We present an architecture of a location tracking system based on the deployment of completely passive monitors that capture WiFi messages transmitted by mobile users for reasons other than localization, such as those messages transmitted for connectivity management (e.g. probe request messages). Being completely passive, our system has the main advantage of being potentially able to track any WiFi equipped devices without the device contributing to the tracking or even being aware of it. However, the feature of complete passivity comes with the challenge of getting accurate localization at regular time intervals, because some devices may not transmit any WiFi message if they are not being actively used. In addition, some messages transmitted by the device might not be captured by monitors due to many reasons such as collision with another message, temporarily changing channel conditions, or software glitches due to the driver of capturing system. The missing of those messages affects the location accuracy of our tracking system because sometimes, the system has to rely on messages captured by less than three monitors. Therefore, we present two techniques to compensate for that missing data by estimating the current position of the user based on its previous positions. The first technique is called Direction and it targets selecting the most probable current position that minimizes the direction change compared to the past positions. The second method is called Speed; it takes as the most probable position the one that leads to the least speed change compared to previous speeds. Both Direction and Speed are inspired from the assumption that humans tend not to make abrupt changes in their speeds and directions while moving under normal circumstances. We evaluate our proposed techniques in comparison with Dead Reckoning technique by simulation with computer generated mobility data and with real mobility data from the CRAWDAD project. By using NS3 we evaluated our techniques with log-normal and indoor propagation models. NS3 simulations on both log-normal and indoor propagation models show that both methods can lead to satisfactory results and missing of data can be compensated by the proposed heuristics.