Paper Info
Title
Cluster filtered KNN: A WLAN-based indoor positioning scheme
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.
Year
DOI
Venue
2008
10.1109/WOWMOM.2008.4594840
WoWMoM
Keywords
Field
DocType
fingerprint,different cluster,estimated result,wlan,final estimate,cluster filtering,cluster approximation,k nearest neighbors,knn,nearest k neighbor,effective positioning system,outperform knn,basic deterministic fingerprint,wlan-based indoor positioning,filtering theory,indoor positioning,position control,wireless lan,location based service,wlan-based indoor positioning scheme,economic indicators,fingerprint recognition,databases,ubiquitous computing,k nearest neighbor,wireless communication,bluetooth
k-nearest neighbors algorithm,Data mining,Wireless,Computer science,Fingerprint recognition,Delegate,Computer network,Location-based service,Cluster analysis,Bluetooth,Positioning system
Conference
ISBN
Citations 
PageRank 
978-1-4244-2100-8
27
1.67
References 
Authors
10
4
Name
Order
Citations
PageRank
Jun Ma1426.93
Xuansong Li2729.93
Xianping Tao374148.91
Jian Lü4139397.91