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Proceedings ArticleDOI

The Horus WLAN location determination system

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TLDR
The Horus system identifies different causes for the wireless channel variations and addresses them and uses location-clustering techniques to reduce the computational requirements of the algorithm and the lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.
Abstract
We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.

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Citations
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SpotFi: Decimeter Level Localization Using WiFi

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Zee: zero-effort crowdsourcing for indoor localization

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

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Indoor localization without the pain

TL;DR: Despite the absence of any explicit pre-deployment calibration, EZ yields a median localization error of 2m and 7m in a small building and a large building, which is only somewhat worse than the 0.7m and 4m yielded by the best-performing but calibration-intensive Horus scheme from prior work.
References
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Proceedings ArticleDOI

RADAR: an in-building RF-based user location and tracking system

TL;DR: RADAR is presented, a radio-frequency (RF)-based system for locating and tracking users inside buildings that combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications.
Journal ArticleDOI

The active badge location system

TL;DR: A novel system for the location of people in an office environment is described, where members of staff wear badges that transmit signals providing information about their location to a centralized location service, through a network of sensors.
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The Cricket location-support system

TL;DR: The randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy are described.

A Survey of Context-Aware Mobile Computing Research

TL;DR: This survey of research on context-aware systems and applications looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context.
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