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Proceedings Paper

Design and simulation of sensor networks for tracking Wifi users in outdoor urban environments
Author(s): Christopher Thron; Khoi Tran; Douglas Smith; Daniel Benincasa
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Paper Abstract

We present a proof-of-concept investigation into the use of sensor networks for tracking of WiFi users in outdoor urban environments. Sensors are fixed, and are capable of measuring signal power from users’ WiFi devices. We derive a maximum likelihood estimate for user location based on instantaneous sensor power measurements. The algorithm takes into account the effects of power control, and is self-calibrating in that the signal power model used by the location algorithm is adjusted and improved as part of the operation of the network. Simulation results to verify the system’s performance are presented. The simulation scenario is based on a 1.5 km2 area of lower Manhattan, The self-calibration mechanism was verified for initial rms (root mean square) errors of up to 12 dB in the channel power estimates: rms errors were reduced by over 60% in 300 track-hours, in systems with limited power control. Under typical operating conditions with (without) power control, location rms errors are about 8.5 (5) meters with 90% accuracy within 9 (13) meters, for both pedestrian and vehicular users. The distance error distributions for smaller distances (<30 m) are well-approximated by an exponential distribution, while the distributions for large distance errors have fat tails. The issue of optimal sensor placement in the sensor network is also addressed. We specify a linear programming algorithm for determining sensor placement for networks with reduced number of sensors. In our test case, the algorithm produces a network with 18.5% fewer sensors with comparable accuracy estimation performance. Finally, we discuss future research directions for improving the accuracy and capabilities of sensor network systems in urban environments.

Paper Details

Date Published: 2 May 2017
PDF: 13 pages
Proc. SPIE 10206, Disruptive Technologies in Sensors and Sensor Systems, 102060L (2 May 2017); doi: 10.1117/12.2262545
Show Author Affiliations
Christopher Thron, Texas A&M Univ.-Central Texas (United States)
Khoi Tran, Texas A&M Univ.-Central Texas (United States)
Douglas Smith, Air Force Research Lab. (United States)
Daniel Benincasa, SUNY Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 10206:
Disruptive Technologies in Sensors and Sensor Systems
Russell D. Hall; Misty Blowers; Jonathan Williams, Editor(s)

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