<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haeberlen, A.</style></author><author><style face="normal" font="default" size="100%">Flannery, E.</style></author><author><style face="normal" font="default" size="100%">Ladd, A. M.</style></author><author><style face="normal" font="default" size="100%">Rudys, A.</style></author><author><style face="normal" font="default" size="100%">Wallach, D. S.</style></author><author><style face="normal" font="default" size="100%">L. E. Kavraki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Practical Robust Localization over Large-Scale 802.11 Wireless Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Tenth ACM International                  Conference on Mobile Computing and Networking                  (MOBICOM 2004)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">kavrakilab</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 26 - Oct. </style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/1023720.1023728</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Philadelphia, PA</style></pub-location><pages><style face="normal" font="default" size="100%">70-84</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures.</style></abstract><work-type><style face="normal" font="default" size="100%">inproceedings</style></work-type></record></records></xml>
