Floor Identification in Large-Scale Environments With Wi-Fi Autonomous Block Models
文献类型：期刊论文
| 作者 | Shao, Wenhua4,5; Luo, Haiyong2,3; Zhao, Fang5; Tian, Hui4; Huang, Jingyu5; Crivello, Antonino1 |
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 18期号:2页码:847-858 |
| ISSN号 | 1551-3203 |
| 关键词 | Autonomous block fingerprint floor identification multistorey buildings smartphone Wi-Fi model |
| DOI | 10.1109/TII.2021.3074153 |
| 英文摘要 | 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%. |
| 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 |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000712564700016 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17900]  |
| 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.CNR, Inst Informat Sci & Technol, I-56124 Pisa, Italy 2.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100876, Peoples R China 4.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China 5.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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