NDLI: Cluster filtered KNN: A WLAN-based indoor positioning scheme

Content Provider	IEEE Xplore Digital Library
Author	Jun Ma Xuansong Li Xianping Tao Jian Lu
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Nanjing Univ., Nanjing (Jun Ma; Xuansong Li; Xianping Tao; Jian Lu)
Abstract	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.
Starting Page	1
Ending Page	8
File Size	516710
Page Count	8
File Format	PDF
ISBN	9781424420995
DOI	10.1109/WOWMOM.2008.4594840
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wireless communication Wireless LAN Databases Buildings Fingerprint recognition Distance measurement Nearest neighbor searches
Content Type	Text
Resource Type	Article

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