Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation Using Wi-Fi Fingerprinting Based on Deep Neural Networks. Issue 5 (3rd September 2018)

• Record Type: Journal Article
• Authors: Kim, Kyeong Soo; Wang, Ruihao; Zhong, Zhenghang; Tan, Zikun; Song, Haowei; Cha, Jaehoon; Lee, Sanghyuk
• Is Part Of: Fiber and integrated optics. Volume 37:Issue 5(2018)
• Page Start: 277
• Page End: 289
• Publication Date: 2018-09-03
• Subjects: Deep learning -- Indoor localization -- multi-class classification -- multi-label classification -- Wi-Fi fingerprinting; Fiber optics -- Periodicals; Integrated optics -- Periodicals; Optical communications -- Periodicals; 621.3692
• DOI: 10.1080/01468030.2018.1467515
• Languages: English
• ISSNs: 0146-8030

Abstract

ABSTRACT: We report the results of our investigation on the use of deep neural networks (DNNs) for building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting. We propose a new DNN architecture based on a stacked autoencoder for feature space dimension reduction 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 demonstrate a prototype system for floor-level location estimation using received signal strengths measured on XJTLU campus. Our results show the strengths of DNN-based approaches, providing near state-of-the-art performance with less parameter tuning and higher scalability.