Research Article
Feature-Based Room-Level Localization of Unmodified Smartphones
@INPROCEEDINGS{10.1007/978-3-319-33681-7_11, author={Jiaxing Shen and Jiannong Cao and Xuefeng Liu and Jiaqi Wen and Yuanyi Chen}, title={Feature-Based Room-Level Localization of Unmodified Smartphones}, proceedings={Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers}, proceedings_a={SMARTCITY360}, year={2016}, month={6}, keywords={Room-level localization RSS Fingerprinting}, doi={10.1007/978-3-319-33681-7_11} }- Jiaxing Shen
Jiannong Cao
Xuefeng Liu
Jiaqi Wen
Yuanyi Chen
Year: 2016
Feature-Based Room-Level Localization of Unmodified Smartphones
SMARTCITY360
Springer
DOI: 10.1007/978-3-319-33681-7_11
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.