Collaborative localization: enhancing WiFi-based position estimation with neighborhood links in clusters

  • Authors:

  • Li-wei Chan;Ji-rung Chiang;Yi-chao Chen;Chia-nan Ke;Jane Hsu;Hao-hua Chu

  • Affiliations:

  • Graduate Institute of Networking and Multimedia, Department of Computer Science and Information Engineering, National Taiwan University;Graduate Institute of Networking and Multimedia, Department of Computer Science and Information Engineering, National Taiwan University;Graduate Institute of Networking and Multimedia, Department of Computer Science and Information Engineering, National Taiwan University;Graduate Institute of Networking and Multimedia, Department of Computer Science and Information Engineering, National Taiwan University;Graduate Institute of Networking and Multimedia, Department of Computer Science and Information Engineering, National Taiwan University;Graduate Institute of Networking and Multimedia, Department of Computer Science and Information Engineering, National Taiwan University

  • Venue:
  • PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
  • Year:
  • 2006

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Abstract

Location-aware services can benefit from accurate and reliable indoor location tracking. The widespread adoption of 802.11x wireless LAN as the network infrastructure creates the opportunity to deploy WiFi-based location services with few additional hardware costs. While recent research has demonstrated adequate performance, localization error increases significantly in crowded and dynamic situations due to electromagnetic interferences. This paper proposes collaborative localization as an approach to enhance position estimation by leveraging more accurate location information from nearby neighbors within the same cluster. The current implementation utilizes ZigBee radio as the neighbor-detection sensor. This paper introduces the basic model and algorithm for collaborative localization. We also report experiments to evaluate its performance under a variety of clustering scenarios. Our results have shown 28.2-56% accuracy improvement over the baseline system Ekahau, a commercial WiFi localization system.