  Device-free wireless indoor localization is an essential technology for the
Internet of Things (IoT), and fingerprint-based methods are widely used. A
common challenge to fingerprint-based methods is data collection and labeling.
This paper proposes a few-shot transfer learning system that uses only a small
amount of labeled data from the current environment and reuses a large amount
of existing labeled data previously collected in other environments, thereby
significantly reducing the data collection and labeling cost for localization
in each new environment. The core method lies in graph neural network (GNN)
based few-shot transfer learning and its modifications. Experimental results
conducted on real-world environments show that the proposed system achieves
comparable performance to a convolutional neural network (CNN) model, with 40
times fewer labeled data.
