5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

Research Article

A Statistical Indoor Localization Method for Supporting Location-based Access Control (Invited Paper)

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  • @INPROCEEDINGS{10.4108/ICST.QSHINE2008.3925,
        author={Chunwang Gao and Zhen Yu and Yawen Wei and Steve Russell and Yong Guan},
        title={A Statistical Indoor Localization Method for Supporting Location-based Access Control (Invited Paper)},
        proceedings={5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness},
        publisher={ICST},
        proceedings_a={QSHINE},
        year={2010},
        month={5},
        keywords={Statistical indoor localization wireless signal strength location- based access control},
        doi={10.4108/ICST.QSHINE2008.3925}
    }
    
  • Chunwang Gao
    Zhen Yu
    Yawen Wei
    Steve Russell
    Yong Guan
    Year: 2010
    A Statistical Indoor Localization Method for Supporting Location-based Access Control (Invited Paper)
    QSHINE
    ICST
    DOI: 10.4108/ICST.QSHINE2008.3925
Chunwang Gao1,*, Zhen Yu1,*, Yawen Wei1,*, Steve Russell1,*, Yong Guan1,*
  • 1: Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
*Contact email: gaojerry@gmail.com, yuzhen@iastate.edu, weiyawen@iastate.edu, sfr@iastate.edu, yguan@iastate.edu

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 in general indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. In an offline phase, we fit a LOESS [3, 4, 16] local regression model on a training set to build a radio map containing the distribution of signal strength. In an online phase, we estimate locations using Maximum Likelihood Estimation (MLE) [7, 8, 9] based on the measured signal strength and the stored distribution. A Bootstrapping method [11] is further exploited to give a confidence interval of estimation. Compared with others, our method is simpler, more systematic and more accurate. Experimental results show that the average error of our method is less than 2m. Hence, it can better support LBAC applications.