Practical robust localization over large-scale 802.11 wireless networks

DMCA

Practical robust localization over large-scale 802.11 wireless networks

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Andreas Haeberlen , Algis Rudys , Eliot Flannery , Dan S. Wallach , Andrew M. Ladd , Lydia E. Kavraki

Venue:	in Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MOBICOM
Citations:	189 - 2 self

BibTeX

@INPROCEEDINGS{Haeberlen_practicalrobust,
    author = {Andreas Haeberlen and Algis Rudys and Eliot Flannery and Dan S. Wallach and Andrew M. Ladd and Lydia E. Kavraki},
    title = {Practical robust localization over large-scale 802.11 wireless networks},
    booktitle = {in Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MOBICOM},
    year = {},
    pages = {70--84}
}

OpenURL

Abstract

We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of human labor to build a detailed signal map, we can train our system by spending less than one minute per office or region, walking around with a laptop and recording the observed signal intensities of our building’s unmodified base stations. We actually collected over two minutes of data per office or region, about 28 man-hours of effort. Using less than half of this data to train the localizer, we can localize a user to the precise, correct location in over 95 % of our attempts, across the entire building. Even in the most pathological cases, we almost never localize a user any more distant than to the neighboring office. A user can obtain this level of accuracy with only two or three signal intensity measurements, allowing for a high frame rate of localization results. Furthermore, with a brief calibration period, our system can be adapted to work with previously unknown user hardware. We present results demonstrating the robustness of our system against a variety of untrained time-varying phenomena, including the presence or absence of people in the building across the day. Our system is sufficiently robust to enable a variety of locationaware applications without requiring special-purpose hardware or complicated training and calibration procedures.

Keyphrases

practical robust localization wireless network entire building accurate localization neighboring office observed signal intensity high frame rate human labor probabilistic technique correct location calibration procedure building unmodified base station detailed signal map untrained time-varying phenomenon built-in signal intensity meter unknown user hardware prior system localization result locationaware application present result signal intensity measurement brief calibration period entire office building pathological case special-purpose hardware significant investment

DMCA