Few-Shot Transfer Learning for Device-Free Fingerprinting Indoor Localization
This addresses data efficiency for IoT localization systems, but it is incremental as it builds on existing transfer learning and GNN methods.
The paper tackles the high data collection and labeling cost in device-free fingerprinting indoor localization by proposing a few-shot transfer learning system that uses a small amount of new labeled data and reuses existing data from other environments, achieving comparable performance to a CNN model with 40 times fewer labeled data.
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.