extendGAN+: Transferable Data Augmentation Framework Using WGAN-GP for Data-Driven Indoor Localisation Model

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 9; p. 4402
• Main Authors: Yean, Seanglidet, Goh, Wayne, Lee, Bu-Sung, Oh, Hong Lye
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30-04-2023; MDPI
• Subjects: Accuracy; Analysis; convolutional neural network; Data augmentation; Data collection; Data entry; Datasets; Deep learning; Dirichlet problem; generative adversarial networks (GANs); indoor localisation; Information management; Localization; Machine learning; Neural networks; Prediction models; received signal strength; Signal strength; Synthetic data; transfer learning; Wi-Fi; Canada; New Jersey; transfer learning; generative adversarial networks (GANs); convolutional neural network; indoor localisation; received signal strength
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23094402

For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Strength (RSS) could easily be affected by obstacles and other factors. In this paper, we propose an extendGAN+ pipeline that leverages up-sampling with the Dirichlet distribution to improve location prediction accuracy with small sample sizes, applies transferred WGAN-GP for synthetic data generation, and ensures data quality with a filtering module. The results highlight the effectiveness of the proposed data augmentation method not only by localisation performance but also showcase the variety of RSS patterns it could produce. Benchmarking against the baseline methods such as fingerprint, random forest, and its base dataset with localisation models, extendGAN+ shows improvements of up to 23.47%, 25.35%, and 18.88% respectively. Furthermore, compared to existing GAN+ methods, it reduces training time by a factor of four due to transfer learning and improves performance by 10.13%.