Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization

TL;DR

This paper introduces a semi-supervised GAN-based indoor localization system that reduces labeling effort and maintains high accuracy, outperforming supervised methods especially with limited labeled data.

Contribution

It presents a novel semi-supervised GAN framework for device-free fingerprinting indoor localization, reducing data labeling costs while maintaining high localization accuracy.

Findings

Achieves comparable performance with sufficient labeled data

Outperforms supervised methods with limited labeled data

Maintains performance across various amounts of labeled data

Abstract

Device-free wireless indoor localization is a key enabling technology for the Internet of Things (IoT). Fingerprint-based indoor localization techniques are a commonly used solution. This paper proposes a semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system. The proposed system uses a small amount of labeled data and a large amount of unlabeled data (i.e., semi-supervised), thus considerably reducing the expensive data labeling effort. Experimental results show that, as compared to the state-of-the-art supervised scheme, the proposed semi-supervised system achieves comparable performance with equal, sufficient amount of labeled data, and significantly superior performance with equal, highly limited amount of labeled data. Besides, the proposed semi-supervised system retains its performance over a broad range of the amount of labeled data. The interactions between the generator, discriminator, and classifier models of the proposed GAN-based system are visually examined and discussed. A mathematical description of the proposed system is also presented.