GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals

doi:10.1109/ICDCS54860.2022.00105

科研成果详情

题名	GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals
作者	Zhuo，Weipeng 1; Zhao，Ziqi 1; Ho Chiu，Ka 1; Li，Shiju 3; Ha，Sangtae 2; Lee，Chul Ho 3; Gary Chan，S. H.1
发表日期	2022
会议名称	42nd IEEE International Conference on Distributed Computing Systems, ICDCS 2022
会议录名称	Proceedings - International Conference on Distributed Computing Systems
卷号	2022-July
页码	1051-1061
会议日期	2022-07-10——2022-07-13
会议地点	Bologna
摘要	We study the problem of floor identification for radiofrequency (RF) signal samples obtained in a crowdsourced manner, where the signal samples are highly heterogeneous and most samples lack their floor labels. We propose GRAFICS, a graph embedding-based floor identification system. GRAFICS first builds a highly versatile bipartite graph model, having APs on one side and signal samples on the other. GRAFICS then learns the low-dimensional embeddings of signal samples via a novel graph embedding algorithm named E-LINE. GRAFICS finally clusters the node embeddings along with the embeddings of a few labeled samples through a proximity-based hierarchical clustering, which eases the floor identification of every new sample. We validate the effectiveness of GRAFICS based on two large-scale datasets that contain RF signal records from 204 buildings in Hangzhou, China, and five buildings in Hong Kong. Our experiment results show that GRAFICS achieves highly accurate prediction performance with only a few labeled samples (96% in both micro- and macro-F scores) and significantly outperforms several state-of-the-art algorithms (by about 45% improvement in micro-F score and 53% in macro-F score).
关键词	Floor identification, graph embedding, crowdsourced RF signals
DOI	10.1109/ICDCS54860.2022.00105
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-85140421685
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/13686
专题	个人在本单位外知识产出
作者单位	1.The Hong Kong University of Science and Technology,Hong Kong 2.University of Colorado Boulder,United States 3.Texas State University,United States
推荐引用方式 GB/T 7714	Zhuo，Weipeng,Zhao，Ziqi,Ho Chiu，Kaet al. GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals[C], 2022: 1051-1061.