Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68339
Title: Feature-based room-level localization of unmodified smartphones
Authors: Shen, JX 
Cao, JN 
Liu, XF 
Wen, JQ 
Chen, YY
Keywords: Room-level localization
RSS
Fingerprinting
Issue Date: 2016
Source: Lecture notes of the Institute for Computer Sciences, Social-informatics and telecommunications engineering, SMART CITY 360, 2016, v. 166, p. 125-136 How to cite?
Journal: Lecture notes of the Institute for Computer Sciences, Social-informatics and telecommunications engineering 
Abstract: Locating smartphone users will enable numerous potential applications such as monitoring customers in shopping malls. However, conventional received signal strength (RSS)-based room-level localization methods are not likely to distinguish neighboring zones accurately due to similar RSS fingerprints. We solve this problem by proposing a system called feature-based room-level localization (FRL). FRL is based on an observation that different rooms vary in internal structures and human activities which can be reflected by RSS fluctuation ranges and user dwell time respectively. These two features combing with RSS can be exploited to improve the localization accuracy. To enable localization of unmodified smartphones, FRL utilizes probe requests, which are periodically broadcast by smartphones to discover nearby access points (APs). Experiments indicate that FRL can reliably locate users in neighboring zones and achieve a 10% accuracy gain, compared with conventional methods like the histogram method.
Description: International Conference on Smart Urban Mobility Services (SUMS), Toronto, CANADA, OCT 13-14, 2015
URI: http://hdl.handle.net/10397/68339
ISBN: 978-3-319-33681-7 (eisbn)
978-3-319-33680-0
ISSN: 1867-8211
DOI: 10.1007/978-3-319-33681-7_11
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