Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation Using Wi-Fi Fingerprinting Based on Deep Neural Networks

Abstract

One of key technologies for future large-scale location-aware services in access is a scalable indoor localization technique. In this paper, we report preliminary results from our investigation on the use of deep neural networks (DNNs) for hierarchical building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting, which we carried out as part of a feasibility study project on Xi'an Jiaotong-Liverpool University (XJTLU) Campus Information and Visitor Service System. To take into account the hierarchical nature of the building/floor classification problem, we propose a new DNN architecture based on a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification with arg max functions to convert multi-label classification results into multi-class classification ones. We also describe the demonstration of a prototype DNN-based indoor localization system for floor-level location estimation using real received signal strength (RSS) data collected at one of the buildings on the XJTLU campus. The preliminary results for both building/floor classification and floor-level location estimation clearly show the strengths of DNN-based approaches, which can provide near state-of-the-art performance with less parameter tuning and higher scalability.

An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms

“…DNN has been proposed as a new strategy due to the fact that it helps to handle traditional learning problems. The discussion in [12] shows that DNN is used for easy adaptation of data variations, management of the dins of Wi-Fi signal, and device and time obsessions of wireless signal because it has advanced learning capability from complex and larger datasets. According to [17,27,28] discussions, the DNN is used to reduce positioning workloads, to improve the accuracy of Wi-Fi-based positioning, and to provide efficient positioning services, because DNN has deeper learning capabilities and efficient prediction performances.…”

“…Additionally, system performances for indoor and outdoor positioning were evaluated at different numbers of layers and neurons, which makes it difficult to reach concise points about system performances. Kim et al [12] used DNN for indoor positioning via radio signal values. In this work, system performance is evaluated by comparing epoch sizes only.…”

“…However, Wi-Fi is common in IoT eras, because we can mount the Wi-Fi connections in various locations without any new infrastructures and additional cost. Besides, data collection using Wi-Fi devices is cheaper, for example, using access points (APs) [12].…”

“…Contrarily, RSS-based positioning technology uses RSS to find the users' position. RSS-based positioning technology has advantages of lower cost, fewer operational complexity, lower power consumption, and easiness in mapping into different areas [12,16]. Positioning using RSS in wireless environments helps to provide location services without additional sensor costs.…”

“…In this approach, the algorithms can obtain the precise position of Wi-Fi users. The scene analysis is characterized by low cost, high precision, and low-energy consumption, so that this method gets much attention [7,8,12,16].…”