Few-Shot Transfer Learning for Device-Free Fingerprinting Indoor Localization

TL;DR

This paper introduces a few-shot transfer learning approach using graph neural networks to significantly reduce data collection costs in device-free indoor localization, achieving high accuracy with minimal labeled data.

Contribution

It presents a novel GNN-based few-shot transfer learning system that leverages existing data to minimize new data collection for indoor localization.

Findings

Achieves comparable accuracy to CNN with 40 times less labeled data

Reduces data collection and labeling costs significantly

Effective in real-world indoor environments

Abstract

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.