Floor Identification in Large-Scale Environments With Wi-Fi Autonomous Block Models

doi:10.1109/TII.2021.3074153

	Floor Identification in Large-Scale Environments With Wi-Fi Autonomous Block Models
	Shao, Wenhua 1,2; Luo, Haiyong 3,4; Zhao, Fang 1; Tian, Hui 2; Huang, Jingyu 1; Crivello, Antonino 5
	2022-02-01
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN	1551-3203
卷号	18 期号:2 页码:847-858
摘要	Traditional Wi-Fi-based floor identification methods mainly have been tested in small experimental scenarios, and generally, their accuracies drop significantly when applied in real large and multistorey environments. The main challenge emerges when the complexity of Wi-Fi signals on the same floor exceeds the complexity between the floors along the vertical direction, leading to a reduced floor distinguishability. A second challenge regards the complexity of Wi-Fi features in environments with atrium, hollow areas, mezzanines, intermediate floors, and crowded signal channels. In this article, we propose an adaptive Wi-Fi-based floor identification algorithm to achieve accurate floor identification also in these environments. Our algorithm, based on the Wi-Fi received signal strength indicator and spatial similarity, first identifies autonomous blocks parcelling the whole environment. Then, local floor identification is performed through the proposed Wi-Fi models to fully harness the Wi-Fi features. Finally, floors are estimated through the joint optimization of the autonomous blocks and the local floor models. We have conducted extensive experiments in three real large and multistorey buildings greater than 140 000 m(-2) using 19 different devices. Finally, we show a comparison between our proposal and other state-of-the-art algorithms. Experimental results confirm that our proposal performs better than other methods, and it exhibits an average accuracy of 97.24%.
关键词	Autonomous block fingerprint floor identification multistorey buildings smartphone Wi-Fi model
DOI	10.1109/TII.2021.3074153
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2019YFC1511400] ; Action Plan Project of the Beijing University of Posts and Telecommunications ; Fundamental Research Funds for the Central Universities[2019XD-A06] ; National Natural Science Foundation of China[61872046] ; Joint Research Fund for Beijing Natural Science Foundation ; Haidian Original Innovation[L192004] ; Beijing Natural Science Foundation[4212024] ; Key Research and Development Project from Hebei Province[19210404D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:000712564700016
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17901
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Luo, Haiyong; Zhao, Fang
作者单位	1.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100876, Peoples R China 4.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100876, Peoples R China 5.CNR, Inst Informat Sci & Technol, I-56124 Pisa, Italy
推荐引用方式 GB/T 7714	Shao, Wenhua,Luo, Haiyong,Zhao, Fang,et al. Floor Identification in Large-Scale Environments With Wi-Fi Autonomous Block Models[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2022,18(2):847-858.
APA	Shao, Wenhua,Luo, Haiyong,Zhao, Fang,Tian, Hui,Huang, Jingyu,&Crivello, Antonino.(2022).Floor Identification in Large-Scale Environments With Wi-Fi Autonomous Block Models.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,18(2),847-858.
MLA	Shao, Wenhua,et al."Floor Identification in Large-Scale Environments With Wi-Fi Autonomous Block Models".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 18.2(2022):847-858.