6th International ICST Conference on Communications and Networking in China

Research Article

Intelligent AP Selection for Indoor Positioning in Wireless Local Area Network

  • @INPROCEEDINGS{10.1109/ChinaCom.2011.6158159,
        author={Zhian Deng and Lin Ma and Yubin Xu},
        title={Intelligent AP Selection for Indoor Positioning in Wireless Local Area Network},
        proceedings={6th International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2012},
        month={3},
        keywords={wireless local area network (wlan) ap selection support vector regression indoor positioning},
        doi={10.1109/ChinaCom.2011.6158159}
    }
    
  • Zhian Deng
    Lin Ma
    Yubin Xu
    Year: 2012
    Intelligent AP Selection for Indoor Positioning in Wireless Local Area Network
    CHINACOM
    IEEE
    DOI: 10.1109/ChinaCom.2011.6158159
Zhian Deng1,*, Lin Ma1, Yubin Xu1
  • 1: Harbin Institute of Technology
*Contact email: dengzhianan@163.com

Abstract

Indoor positioning system in wireless local area network (WLAN) has been receiving increasing interest in pervasive computing applications. To keep balance between energy consumption on client device and positioning accuracy, AP selection strategy is always proposed to select the most discriminant APs for positioning. In this paper, we propose an intelligent AP selection method based on joint location information gain. In contrast to traditional AP selection methods which measure the discriminant ability of APs independently, we consider it jointly. By considering the correlation of the discriminant ability between different APs, more accurate measure of the discriminant ability can be taken. Besides, support vector regression (SVR) positioning algorithm is combined to estimate the location. Experiments are carried in a realistic WLAN indoor environment. Experimental results show that, by using the intelligent AP selection method, the proposed positioning algorithm maintains a high-level accuracy while reducing the energy consumption on client device significantly.