Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/70752
Title: Large scale wireless indoor localization by clustering and extreme learning machine
Authors: Xiao, W.
Liu, P.
Soh, W.-S. 
Huang, G.-B.
Keywords: Clustering
ELM
Scalability
WLAN
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. 15th International Conference on Information Fusion, FUSION 2012 : 1609-1614. ScholarBank@NUS Repository.
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. © 2012 ISIF (Intl Society of Information Fusi).
Source Title: 15th International Conference on Information Fusion, FUSION 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/70752
ISBN: 9780982443859
Appears in Collections:Staff Publications

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