  Human-centered data collection is typically costly and implicates issues of
privacy. Various solutions have been proposed in the literature to reduce this
cost, such as crowdsourced data collection, or the use of semi-supervised
algorithms. However, semi-supervised algorithms require a source of unlabeled
data, and crowd-sourcing methods require numbers of active participants. An
alternative passive data collection modality is fingerprint-based localization.
Such methods use received signal strength (RSS) or channel state information
(CSI) in wireless sensor networks to localize users in indoor/outdoor
environments. In this paper, we introduce a novel approach to reduce training
data collection costs in fingerprint-based localization by using synthetic
data. Generative adversarial networks (GANs) are used to learn the distribution
of a limited sample of collected data and, following this, to produce synthetic
data that can be used to augment the real collected data in order to increase
overall positioning accuracy. Experimental results on a benchmark dataset show
that by applying the proposed method and using a combination of 10% collected
data and 90% synthetic data, we can obtain essentially similar positioning
accuracy to that which would be obtained by using the full set of collected
data. This means that by employing GAN-generated synthetic data, we can use 90%
less real data, thereby reduce data-collection costs while achieving acceptable
accuracy.
