Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102198
Title: Large scale wireless indoor localization by clustering and Extreme Learning Machine
Authors: Xiao, Wendong
Huang, Guang-Bin
Liu, Peidong
Soh, Wee-Seng
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 2012
Source: Xiao, W., Liu, P., Soh, W.-S., & Huang, G.-B. (2012). Large scale wireless indoor localization by clustering and Extreme Learning Machine. 2012 15th International Conference on Information Fusion (FUSION), 1609-1614.
Abstract: Due to the widespread deployment and low cost, WLAN has gained more attention for indoor localization recently. However, when we apply these WLAN based localization algorithms to large-scale environments, such as a wireless city, they may encounter the scalability problem due to the huge RSS database. The huge database may cause long response time for the terminal clients if the localization algorithm needs to search the database for the real time localization phase. In this paper, we propose a novel clustering based localization algorithm for large scale area by utilizing Nearest Neighbor (NN) rule and Extreme Learning Machine (ELM). The proposed algorithm has shown competitive advantage in terms of the real time localization efficiency as well as the localization accuracy.
URI: https://hdl.handle.net/10356/102198
http://hdl.handle.net/10220/19849
Rights: © 2012 International Society of Information Fusion. This paper was published in 2012 15th International Conference on Information Fusion (FUSION) and is made available as an electronic reprint (preprint) with permission of International Society of Information Fusion. The paper can be found at the following official URL: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6290497&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F6269381%2F6289713%2F06290497.pdf%3Farnumber%3D6290497. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

Files in This Item:
File Description SizeFormat 
Large scale wireless indoor localization by clustering and Extreme Learning Machine.pdf871.04 kBAdobe PDFThumbnail
View/Open

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.