Publication Type

Conference Proceeding Article

Publication Date

7-2017

Abstract

While most existing indoor localization techniques are device-based, many emerging applications such as intruder detection and elderly monitoring drive the needs of device-free localization, in which the target can be localized without any device attached. Among the diverse techniques, received signal strength (RSS) fingerprint-based methods are popular because of the wide availability of RSS readings in most commodity hardware. However, current fingerprint-based systems suffer from high human labor cost to update the fingerprint database and low accuracy due to the large degree of RSS variations. In this paper, we propose a fingerprint-based device-free localization system named iUpdater to significantly reduce the labor cost and increase the accuracy. We present a novel self-augmented regularized singular value decomposition (RSVD) method integrating the sparse attribute with unique properties of the fingerprint database. iUpdater is able to accurately update the whole database with RSS measurements at a small number of reference locations, thus reducing the human labor cost. Furthermore, iUpdater observes that although the RSS readings vary a lot, the RSS differences between both the neighboring locations and adjacent wireless links are relatively stable. This unique observation is applied to overcome the short-term RSS variations to improve the localization accuracy. Extensive experiments in three different environments over 3 months demonstrate the effectiveness and robustness of iUpdater.

Keywords

Databases, Matrix decomposition, Wireless fidelity, Sparse matrices, Microwave integrated circuits, Fingerprint recognition, Wireless communication

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE 37th International Conference on Distributed Computing Systems ICDCS 2017: Proceedings: 5-8 June, Atlanta, Georgia

First Page

900

Last Page

910

ISBN

9781538617922

Identifier

10.1109/ICDCS.2017.216

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://doi.org./10.1109/ICDCS.2017.216

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