Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2017

Abstract

The proliferation of wireless technologies in today's everyday life is one of the key drivers of the Internet of Things (IoT). In addition to being an enabler of connectivity, the vast penetration of wireless devices today gives rise to a secondary functionality as a means of tracking and localization of the devices themselves. Indeed, in order to discover and automatically connect to known Wi-Fi networks, mobile devices have to scan and broadcast the so-called probe requests on all available channels, which can be captured and analyzed in a non-intrusive manner. Thus, one of the key applications of this feature is the ability to track and analyze human behaviors in real-time directly from the patterns observed from their Wi-Fi-enabled devices. In this paper, we develop such a system to obtain these Wi-Fi signatures in a completely passive manner and use the Wi-Fi features it captures within a set of adaptive machine learning techniques to predict in real-time the expected length of stay (LOS) of the device owners at a specific location.

Keywords

Wireless fidelity, Mobile handsets, Probes, Real-time systems, Servers, Sensors, Support vector machines

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Publication

ICC 2017: IEEE International Conference on Communications, Paris, France, May 21-25: Proceedings

First Page

7996509-1

Last Page

6

ISBN

9781467389990

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

3-5-2018

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICC.2017.7996509

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