Practical robust localization over large-scale 802.11 wireless networks

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.

User‐Centric Indoor Air‐Quality Monitoring on Mobile Devices

“…Therefore, none of the occurrence-based Wi-Fi fingerprints are good at distinguishing rooms. Our results also confirmed the observation (Haeberlen et al 2004 poral variations, we do see good concentration of same-room curves, especially when ordered k-combination is used. Figure 7f-h shows the CDF curves of RSS-Diff for different time periods, using 1-AP, 2-APs, and ordered 2-APs as fingerprints.…”

GSM indoor localization

“…This was the first fingerprinting system that showed that it is possible to localize a laptop in the hallways of a small office building within 2-3 m of its true location, using fingerprints from four 802.11 access points. There have been improvements to Radar's fingerprint matching algorithm that have improved accuracy [2,13,19] and were able to differentiate between floors of a building with a high degree of precision [8]. In addition, commercial localization products have been built using 802.11 fingerprinting [20].…”

Localization to Enhance Security and Services in Wi-Fi Networks under Privacy Constraints

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