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A Statistical Indoor Localization Method for Supporting Location-based Access Control

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Abstract

Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated to accommodate to different indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. First, in an offline training phase, we fit a locally weighted regression and smoothing scatterplots (LOESS) model on the signal strength received at different training locations, and build a radio map that contains the distribution of signal strength. Then, in an online estimation phase, we determine the locations of unknown points using maximum likelihood estimation (MLE) based on the measured signal strength and the stored distribution. In addition, we provide a 95% confidence interval to our estimation using a Bootstrapping module. Compared with other approaches, our method is simpler, more systematic and more accurate. Experimental results show that the estimation error of our method is less than 2m. Hence, it can better support LBAC applications than others.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, 50011, USA

    Chunwang Gao, Zhen Yu, Yawen Wei, Steve Russell & Yong Guan

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  1. Chunwang Gao
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  2. Zhen Yu
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  3. Yawen Wei
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  4. Steve Russell
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  5. Yong Guan
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Corresponding author

Correspondence to Yong Guan.

Additional information

This work was partially supported by US National Science Foundation (NSF) under grants CNS-0644238 and CNS-0626822. A preliminary version of the paper has appeared in QShine 2008.

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Gao, C., Yu, Z., Wei, Y. et al. A Statistical Indoor Localization Method for Supporting Location-based Access Control. Mobile Netw Appl 14, 253–263 (2009). https://doi.org/10.1007/s11036-008-0143-4

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  • DOI: https://doi.org/10.1007/s11036-008-0143-4

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