DocumentCode :
567650
Title :
Large scale wireless indoor localization by clustering and Extreme Learning Machine
Author :
Wendong Xiao ; Peidong Liu ; Wee-Seng Soh ; Guang-Bin Huang
Author_Institution :
Sch. of Autom. & Electr. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
fYear :
2012
fDate :
9-12 July 2012
Firstpage :
1609
Lastpage :
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.
Keywords :
indoor communication; pattern classification; pattern clustering; wireless LAN; ELM; RSS database; WLAN; clustering; extreme learning machine; large scale wireless indoor localization; nearest neighbor rule; Accuracy; Artificial neural networks; Clustering algorithms; Clustering methods; Databases; Testing; Training; Clustering; ELM; Scalability; WLAN;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2
Type :
conf
Filename :
6290497
Link To Document :
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