FISONE: Floor Identification System with One Label for Crowdsourced RF Signals
Abstract
Floor labels of crowdsourced RF signals are crucial for many smartcity applications, such as multifloor 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 floorlabeled signal sample on the bottom floor while having the rest of signal samples unlabeled. We propose FISONE, a novel floor identification system with only one labeled sample. FISONE 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 attentionbased 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 FISONE and validated its effectiveness on the Microsoft dataset and in three large shopping malls. Our results show that FISONE outperforms other baseline algorithms significantly, with up to 23% improvement in adjusted rand index and 25% improvement in normalized mutual information using only one floorlabeled signal sample.
 Publication:

arXiv eprints
 Pub Date:
 July 2023
 DOI:
 10.48550/arXiv.2307.05914
 arXiv:
 arXiv:2307.05914
 Bibcode:
 2023arXiv230705914Z
 Keywords:

 Computer Science  Networking and Internet Architecture;
 Computer Science  Machine Learning;
 Electrical Engineering and Systems Science  Signal Processing
 EPrint:
 Accepted by IEEE ICDCS 2023