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Traditional machine learning methods require the assumption that training and test data are drawn from the same distribution, which proves challenging in real-world applications. Moreover, deep learning models require a substantial amount of labeled data for training in classification tasks and limited samples may lead to overfitting. In many real-world scenarios, there is an insufficient supply of labeled samples within the target domain for learning. Transfer learning offers an effective solution, allowing knowledge from a source domain to be transferred to a target domain. Additionally, data augmentation enhances model generalization by increasing data samples, particularly beneficial when dealing with limited target domain data. In this paper, we synergistically enhance the model's performance on classification tasks by integrating transfer learning techniques with a data augmentation strategy. By conducting numerous experiments across various datasets, we verified the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Accurate indoor location information in multi-building/floor environments is essential for establishing many indoor location-based services (LBS). Wi-Fi fingerprinting with received signal strength indicators (RSSI) has become one of the most practical techniques to localize users indoors. However, the fluctuation of wireless signals caused by fading, the multipath effect, and device heterogeneity leads to considerable variations in RSSIs, which poses a challenge to accurate localization. This paper proposes an indoor positioning method, which is constructed based on a convolutional neural network (CNN) framework. Specifically, a novel model by combining an extreme learning machine autoencoder (ELM-AE) with a two-dimensional CNN is proposed. The ELM-AE extracts critical features by reducing input dimensions, while the CNN is trained to effectively achieve significant performance in the positioning phase. To increase positioning accuracy and deal with data shortages, a data augmentation strategy by frequently adding noisy data to the original fingerprint map is devised. The statistical properties namely, the RSS values of each MAC address, are utilized to adjust input noise. The performance of the proposed system is evaluated on two Tampere and UJIIndoorLoc datasets. The experimental results show that EA-CNN achieves better performance than CNN by decreasing average positioning error up to 40.95% and 43.74% in Tampere and UJIIndoorLoc datasets, respectively. Compared to state-of-the-art deep learning-based methods, the positioning performance improves up to 68.36% in Tampere and 67.56% in the UJIIndoorLoc dataset by exploiting only 25% of the training samples. Compared to several state-of-the-art deep learning models, EA-CNN achieves higher accuracy in both positioning and floor estimation. • EA-CNN outperform the state-of-the-art CNN-based procedures. • ELM-AE decrease input dimension, and increase positioning performance of CNN. • Augmentation strategy dealing with data shortages, improve the training efficiency. • Large scale environments and crowdsourced data are utilized for evaluation. [ABSTRACT FROM AUTHOR]