Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkIndoor LocalizationSimilar Papers 제목 키워드 기반
The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting
Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characteristics, recent studies demonstrated that us…
Variational InferenceSemi-Supervised RF Fingerprinting with Consistency-Based Regularization
As a promising non-password authentication technology, radio frequency (RF) fingerprinting can greatly improve wireless security. Recent work has shown that RF fingerprinting based on deep learning can significantly outp…
Data AugmentationProtocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting
While supervised deep neural networks (DNNs) have proven effective for device authentication via radio frequency (RF) fingerprinting, they are hindered by domain shift issues and the scarcity of labeled data. The success…
Backdoor AttackWarwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics
Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipelin…
Image ForensicsFingerprinting Image-to-Image Generative Adversarial Networks
Generative Adversarial Networks (GANs) have been widely used in various application scenarios. Since the production of a commercial GAN requires substantial computational and human resources, the copyright protection of …
Image-to-Image Translation