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We study the problem of floor identification for radiofrequency (RF) signalsamples obtained in a crowdsourced manner, where the signal samples are highlyheterogeneous and most samples lack their floor labels. We propose GRAFICS, agraph embedding-based floor identification system. GRAFICS first builds ahighly versatile bipartite graph model, having APs on one side and signalsamples on the other. GRAFICS then learns the low-dimensional embeddings ofsignal samples via a novel graph embedding algorithm named E-LINE. GRAFICSfinally clusters the node embeddings along with the embeddings of a few labeledsamples through a proximity-based hierarchical clustering, which eases thefloor identification of every new sample. We validate the effectiveness ofGRAFICS based on two large-scale datasets that contain RF signal records from204 buildings in Hangzhou, China, and five buildings in Hong Kong. Ourexperiment results show that GRAFICS achieves highly accurate predictionperformance with only a few labeled samples (96% in both micro- and macro-Fscores) and significantly outperforms several state-of-the-art algorithms (byabout 45% improvement in micro-F score and 53% in macro-F score).

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