# Inferring Mobile Trajectories Using a Network of Binary Proximity Sensors

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“ Inferring Mobile Trajectories Using a Network of Binary Proximity
Sensors”
by Eunjoon Cho, Kevin Wong, Omprakash Gnawali, Martin Wicke, and Leonidas Guibas.
In * Proceedings of the 8th Annual IEEE Communications Society
Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON
2011)*, June 2011.

## Abstract

Understanding human mobility in an environment can be approached in many
forms, one of which is to recover the underlying structure of user
movement. In our work, we show that we can use a network of binary
proximity sensors to detect paths between nodes and also extract highly
popular trajectories users take. We show that with sufficient amount of
these binary data, even with no prior knowledge of the location of these
sensors, we can capture a correlation between the detection timestamps in
the case where a physical path exists between any two nodes. Our algorithm
also generates characteristics of the path, such as the distribution of
transition times and volume. We further show that with sampling techniques
we can estimate the underlying trajectories that generated the time
stamps. We have tested our algorithm on a simulator and two sensor network
deployments. We found that, despite the lack of position information about
the sensor nodes, with timestamps alone our algorithm can accurately
detect the trajectories and is robust enough to use in a real-world office
building.

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**BibTeX entry:**

@inproceedings{proximity2011,
author = {Eunjoon Cho, and Kevin Wong and Omprakash Gnawali and Martin
Wicke and Leonidas Guibas},
title = { { Inferring Mobile Trajectories Using a Network of Binary
Proximity Sensors}},
booktitle = { Proceedings of the 8th Annual IEEE Communications Society
Conference on Sensor, Mesh and Ad Hoc Communications and Networks
(SECON 2011)},
month = { June },
year = {2011}
}