Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization
This work addresses the costly data labeling problem in IoT indoor localization, offering an incremental improvement over existing supervised methods.
The paper tackles the problem of reducing data labeling effort in device-free fingerprinting indoor localization for IoT by proposing a semi-supervised GAN-based system that uses a small amount of labeled data and a large amount of unlabeled data. It achieves comparable performance to state-of-the-art supervised methods with sufficient labeled data and significantly superior performance with highly limited labeled data.
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.