FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals

doi:10.1109/ICDCS57875.2023.00039

科研成果详情

题名	FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals
作者	Zhuo，Weipeng 1; Chiu，Ka Ho 1; Chen，Jierun 1; Zhao，Ziqi 1; Chan，S. H.Gary 1; Ha，Sangtae 2; Lee，Chul Ho 3
发表日期	2023
会议名称	43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023
会议录名称	Proceedings - International Conference on Distributed Computing Systems
卷号	2023-July
页码	418-428
会议日期	2023-07-18——2023-07-21
会议地点	Hong Kong
摘要	Floor labels of crowdsourced RF signals are crucial for many smart-city applications, such as multi-floor indoor localization, geofencing, and robot surveillance. To build a prediction model to identify the floor number of a new RF signal upon its measurement, conventional approaches using the crowdsourced RF signals assume that at least few labeled signal samples are available on each floor. In this work, we push the envelope further and demonstrate that it is technically feasible to enable such floor identification with only one floor-labeled signal sample on the bottom floor while having the rest of signal samples unlabeled. We propose FIS-ONE, a novel floor identification system with only one labeled sample. FIS-ONE consists of two steps, namely signal clustering and cluster indexing. We first build a bipartite graph to model the RF signal samples and obtain a latent representation of each node (each signal sample) using our attention-based graph neural network model so that the RF signal samples can be clustered more accurately. Then, we tackle the problem of indexing the clusters with proper floor labels, by leveraging the observation that signals from an access point can be detected on different floors, i.e., signal spillover. Specifically, we formulate a cluster indexing problem as a combinatorial optimization problem and show that it is equivalent to solving a traveling salesman problem, whose (near-)optimal solution can be found efficiently. We have implemented FIS-ONE and validated its effectiveness on the Microsoft dataset and in three large shopping malls. Our results show that FIS- ONE outperforms other baseline algorithms significantly, with up to 23 % improvement in adjusted rand index and 25% improvement in normalized mutual information using only one floor-labeled signal sample.
DOI	10.1109/ICDCS57875.2023.00039
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-85175001380
引用统计	被引频次：2[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/13685
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,Chiu，Ka Ho,Chen，Jierunet al. FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals[C], 2023: 418-428.