دورية أكاديمية
A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting
| العنوان: | A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting |
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| المؤلفون: | Kyeong Soo Kim, Sanghyuk Lee, Kaizhu Huang |
| المصدر: | Big Data Analytics, Vol 3, Iss 1, Pp 1-17 (2018) |
| بيانات النشر: | BMC |
| سنة النشر: | 2018 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Multi-building and multi-floor indoor localization, Wi-Fi fingerprinting, Deep learning, Neural networks, Multi-label classification, Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Background One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for the scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built. Results We evaluate the performance of building/floor estimation and floor-level coordinates estimation of a given location using the UJIIndoorLoc dataset covering three buildings with four or five floors in the Jaume I University (UJI) campus, Spain. Experimental results demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN. Conclusions The proposed scalable DNN architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting can achieve near state-of-the-art performance with just a single DNN and enables the implementation with lower complexity and energy consumption at mobile devices. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2058-6345 |
| العلاقة: | http://link.springer.com/article/10.1186/s41044-018-0031-2Test; https://doaj.org/toc/2058-6345Test; https://doaj.org/article/9b73ef2ff03b46a382c475f3b47354ccTest |
| DOI: | 10.1186/s41044-018-0031-2 |
| الإتاحة: | https://doi.org/10.1186/s41044-018-0031-2Test https://doaj.org/article/9b73ef2ff03b46a382c475f3b47354ccTest |
| رقم الانضمام: | edsbas.263B90D5 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 20586345 |
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| DOI: | 10.1186/s41044-018-0031-2 |