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Title:
clustering algorithms research for device-clustering localization
Author: Cheng Huang ; Wang Feng ; Tao Rui ; Luo Haiyong ; Zhao Fang
Source: 2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012 - Conference Proceedings
Conference Name: 2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012
Conference Date: November 13, 2012 - November 15, 2012
Issued Date: 2012
Conference Place: Sydney, NSW, Australia
Indexed Type: EI
ISBN: 9781467319546
Department: (1) Software School Beijing University of Posts and Telecommunications China; (2) Institute of Computing Technology Chinese Academy of Sciences Beijing China
Abstract: Crowdsourcing-based localization has attracted wide research concern to the metropolitan-scale positioning. However, crowdsourcing-based fingerprints collection with assorted mobile smart devices brings fingerprint confusion, which significantly degrades the localization accuracy. To solve the device diversity problem, many solutions have been raised like the Device-Clustering algorithm. Based on macro Device-Cluster (DC) rather than natural device, DC algorithm maintains less device types and slight calibration overhead. Despite high positioning accuracy, the selection of suitable clustering algorithms in DC system becomes another puzzle. In this paper, we reshape the novel Device-Clustering algorithm to enhance the indoor positioning by comparing the application of different clustering algorithms. The experimental result indicates the reliability of DC strategy in broad clustering scheme as well as the suitable locating process corresponding to distinct environment. © 2012 IEEE.
English Abstract: Crowdsourcing-based localization has attracted wide research concern to the metropolitan-scale positioning. However, crowdsourcing-based fingerprints collection with assorted mobile smart devices brings fingerprint confusion, which significantly degrades the localization accuracy. To solve the device diversity problem, many solutions have been raised like the Device-Clustering algorithm. Based on macro Device-Cluster (DC) rather than natural device, DC algorithm maintains less device types and slight calibration overhead. Despite high positioning accuracy, the selection of suitable clustering algorithms in DC system becomes another puzzle. In this paper, we reshape the novel Device-Clustering algorithm to enhance the indoor positioning by comparing the application of different clustering algorithms. The experimental result indicates the reliability of DC strategy in broad clustering scheme as well as the suitable locating process corresponding to distinct environment. © 2012 IEEE.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15955
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Cheng Huang,Wang Feng,Tao Rui,et al. clustering algorithms research for device-clustering localization[C]. 见:2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012. Sydney, NSW, Australia. November 13, 2012 - November 15, 2012.
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