A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting

• Published in: IEEE access Vol. 7; pp. 110698 - 110709
• Main Authors: Song, Xudong, Fan, Xiaochen, Xiang, Chaocan, Ye, Qianwen, Liu, Leyu, Wang, Zumin, He, Xiangjian, Yang, Ning, Fang, Gengfa
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Buildings; Coders; convolutional neural network; Convolutional neural networks; Datasets; deep learning; Electronic devices; Feature extraction; Fingerprint recognition; Fingerprinting; Fingerprints; Floors; Indoor localization; Localization; Location based services; Model testing; Neural networks; Signal strength; Training; WiFi fingerprinting; Wireless communication; Wireless fidelity
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2933921

With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSIndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.