Automatic radio map construction exploiting mobile payments

Abstract

Radio map construction automation by location-labeling of crowdsourced fingerprints is drawing a great attention these days. It allows radio maps of most of buildings in cities to be constructed at a very low cost. This paper proposes an adaptive semi-supervised location-labeling method for the crowdsourced fingerprints. The method is distinguished from the existing semi-supervised learning methods in that it uses address-labeled fingerprints collected during offline mobile payments for its location references. Despite inexactly specified location references, the method finds an optimal placement of location-unlabeled fingerprint sequences by varying the locations of address-labeled fingerprints. When the proposed method was evaluated at three large-scale landmark buildings in Seoul, the effectiveness of using location references collected during mobile payments for the proposed adaptive semi-supervised location-labeling method was apparent. Highly precise radio maps could be constructed for the buildings without any manual calibration efforts. The method can be used to automatically construct radio maps for most downtown buildings.