Cluster filtered KNN: A WLAN-based indoor positioning scheme

Location Based Service (LBS) is one kind of ubiquitous applications whose functions are based on the locations of clients. The core of LBS is an effective positioning system. As wireless LAN (WLAN) costs less and is easy to access, using WLAN for indoor positioning has been widely studied recently. K nearest neighbors (KNN) is one of the basic deterministic fingerprint based algorithms and widely used for WLAN-based indoor positioning. However, KNN takes all the nearest K neighbors for calculating the estimated result, which could be improved if some selective work could be done to those neighbors beforehand. In this paper we propose a new scheme called "cluster filtered KNN" (CFK). CFK utilizes clustering technique to partition those neighbors into different clusters and chooses one cluster as the delegate. In the end, the final estimate can be calculated only based on the elements of the delegate. With experiments, we found that CFK does outperform KNN.

[1]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[2]  Andrew G. Dempster,et al.  Indoor Positioning Techniques Based on Wireless LAN , 2007 .

[3]  Minyi Guo,et al.  Location-Aware Information Retrieval for Mobile Computing , 2004, EUC.

[4]  Haiyun Luo,et al.  Zero-Configuration, Robust Indoor Localization: Theory and Experimentation , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[5]  Polly Huang,et al.  Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics , 2005, MSWiM '05.

[6]  Raffaele Bruno,et al.  Design and Analysis of a Bluetooth-Based Indoor Localization System , 2003, PWC.

[7]  C. Rizos,et al.  Method for yielding a database of location fingerprints in WLAN , 2005 .

[8]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[9]  Jianhua Ma,et al.  A Dangerous Location Aware System for Assisting Kids Safety Care , 2006, 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06).

[10]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[11]  Panos K. Chrysanthis,et al.  On indoor position location with wireless LANs , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[12]  Gergely V. Záruba,et al.  A Bayesian sampling approach to in-door localization of wireless devices using received signal strength indication , 2005, Third IEEE International Conference on Pervasive Computing and Communications.

[13]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[14]  Yufei Wang,et al.  Two New Algorithms for Indoor Wireless Positioning System (WPS) , 2004 .

[15]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[16]  Andy Hopper,et al.  The Anatomy of a Context-Aware Application , 1999, Wirel. Networks.

[17]  M.K. Denko,et al.  IEEE 802.11 WLAN Based Real-Time Location Tracking in Indoor and Outdoor Environments , 2007, 2007 Canadian Conference on Electrical and Computer Engineering.

[18]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .