Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66930
Title: A graph-based method for indoor subarea localization with zero-configuration
Authors: Chen, YY
Guo, MY
Shen, JX
Cao, JN 
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), p. 236-244 How to cite?
Abstract: Indoor subarea localization remains an open problem due to existing studies face two main bottlenecks, one is fingerprint-based methods require time-consuming site survey and another is triangulation-based methods is lack of scalability in large-scale environment. In this paper, we aim to present a graph-based method for indoor subarea localization with zero-configuration, which can be directly employed without offline manually constructing fingerprint map or pre-installing additional infrastructure. To accomplish this, we first utilize two unexploited characteristics of WiFi radio signal strength to generate logical floor graph, and then formulate the problem of constructing fingerprint map in terms of a graph isomorphism problem between logical floor graph and physical floor graph. Then, a Bayesian-based approach is utilized to estimate the unknown subarea in online localization. The proposed method has been implemented in a real-world shopping mall and extensive experimental results show that our method can achieve competitive performance comparing with existing methods.
URI: http://hdl.handle.net/10397/66930
ISBN: 978-1-5090-2771-2
DOI: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0054
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