SoN: Selective Optimal Network for smartphone-based indoor localization in real-time

Deep learning-based scene recognition algorithms have been developed for real-time application in indoor localization systems. However, owing to the slow calculation time resulting from the deep structure of convolutional neural networks , deep learning-based algorithms have limitations in the usage of real-time applications, despite their high accuracy in classification tasks . To significantly reduce the computation time of these algorithms and slightly improve their accuracy, we thus propose a path-selective deep learning network, denoted as Selective Optimal Network (SoN). The SoN selectively uses the depth-variable networks depending on a new indicator, denoted as the classification-complexity of a source image. The SoN reduces the prediction time by selecting optimal depth for the baseline networks corresponding to the input samples. The network was evaluated using two public datasets and two custom datasets for indoor localization and scene classification, respectively. The experimental results indicated that, compared to other deep learning models, the SoN exhibited improved accuracy and enhanced the processing speed by up to 78.59%. Additionally, the SoN was applied to a smartphone-based indoor positioning system in real-time. The results indicated that the SoN shows excellent performance for rapid and accurate classification in real-time applications of indoor localization systems. • A novel framework, Selective Optimal Network (henceforth SoN), is proposed for high-speed classification applications. • An effective path selection algorithm is developed for the SoN to help it select an optimal network for input samples according to their complexity in the classification. • Additionally, classification-complexity , quantifying the level of classification-complexity for an input image, is proposed to select the optimal backbone architecture from ones of different depths. • The semi-supervised methodology to search the optimal classification-complexity corresponding to the individual images is devised in order to fine-tune the optimal level of difficulty while training SoN. • Generally, the SoN for the smartphone-based IPS provides faster prediction time (78.59%) and better accuracy (1.50%) than other SotA models.