Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation using Wi-Fi Fingerprinting Based on Deep Neural Networks
| Autor: | Kim, Kyeong Soo, Wang, Ruihao, Zhong, Zhenghang, Tan, Zikun, Song, Haowei, Cha, Jaehoon, Lee, Sanghyuk |
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| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Fiber and Integrated Optics, pp. 1-13, Apr. 27, 2018 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1080/01468030.2018.1467515 |
| Popis: | 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 argmax 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. Comment: 5 pages, 6 figures, FOAN 2017 (Munich, Germany, Oct. 2017) |
| Databáze: | arXiv |
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