Cnnloc: Deep-learning based indoor localization with wifi fingerprinting

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019, 2019, pp. 589-595
Issue Date:
2019-08-01
Filename Description Size
CNNLoc-Main.pdfAccepted manuscript2.05 MB
Adobe PDF
Full metadata record
© 2019 IEEE. 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 computationintensive, 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 multifloor localization. Specifically, we devise a novel classification model by combining a Stacked Auto-Encoder (SAE) with a onedimensional 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 success rates in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset with several stateof-the-art methods. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on buildinglevel localization and floor-level localization, respectively.
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