clustering algorithms research for device-clustering localization

ISCAS OpenIR

	clustering algorithms research for device-clustering localization
	Cheng Huang; Wang Feng; Tao Rui; Luo Haiyong; Zhao Fang
	2012
会议名称	2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012
会议录名称	2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012 - Conference Proceedings
页码	-
会议日期	November 13, 2012 - November 15, 2012
会议地点	Sydney, NSW, Australia
收录类别	EI
ISBN	9781467319546
部门归属	(1) Software School Beijing University of Posts and Telecommunications China; (2) Institute of Computing Technology Chinese Academy of Sciences Beijing China
摘要	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.; 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.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15955
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Cheng Huang,Wang Feng,Tao Rui,et al. clustering algorithms research for device-clustering localization[C],2012:-.