FIS-ONE: Floor Identification System with One Label for Crowdsourced RF
  Signals
Weipeng Zhuo Ka Ho Chiu Jierun Chen Ziqi Zhao S. -H. Gary Chan Sangtae Ha Chul-Ho Lee
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.