GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals
GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals
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DOI：
10.1109/icdcs54860.2022.00105
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Weipeng Zhuo;Ziqi Zhao;Ka Ho Chiu;Shiju Li;Sangtae Ha;Chul-Ho Lee;S. G. Gary Chan
中科院分区：
文献类型：
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作者：
Weipeng Zhuo;Ziqi Zhao;Ka Ho Chiu;Shiju Li;Sangtae Ha;Chul-Ho Lee;S. G. Gary Chan
我们研究了众包方式获得的射频（RF）信号样本的楼层识别问题，其中信号样本是高度异构的，大多数样本缺乏楼层标签。我们提出了GRAFICS，一个基于图嵌入的楼层识别系统。GRAFICS首先构建了一个高度通用的二分图模型，一边是AP，另一边是信号样本。GRAFICS然后通过一种名为E-LINE的新的图嵌入算法学习信号样本的低维嵌入。GRAFICS最后通过基于邻近度的层次聚类将节点嵌入沿着少量标记样本的嵌入进行聚类，从而简化了每个新样本的楼层识别。我们验证了GRAFICS的有效性的基础上，两个大规模的数据集，包含RF信号记录，从204个建筑物在中国杭州，和5个建筑物在香港。我们的实验结果表明，GRAFICS实现了高度准确的预测性能，只有很少的标记样本（96%的微观和宏观F分数），并显着优于几个国家的最先进的算法（约45%的改善微观F分数和53%的宏观F分数）。
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).