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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. | Conference: | International Conference on Information Fusion (FUSION) (15th : 2012 : Singapore) | 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 |
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 | Schools: | School of Electrical and Electronic Engineering | 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 |
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