CNNLoc: Deep-Learning Based Indoor Localization with WiFi Fingerprinting

Xudong Song, Xiaochen Fan, Xiangjian He, Chaocan Xiang, Qianwen Ye, Xiang Huang, Gengfa Fang, Liming Luke Chen, Jing Qin, Zumin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publication2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Place of PublicationLeicester UK
PublisherIEEE Xplore
Pages589-595
Number of pages6
ISBN (Electronic)978-1-7281-4034-6
ISBN (Print)978-1-7281-4035-3
DOIs
Publication statusPublished - 19 Aug 2019
Event2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - Leicester, United Kingdom
Duration: 19 Aug 201923 Aug 2019

Conference

Conference2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Period19/08/1923/08/19

Keywords

  • indoor localization
  • deep learning
  • Convolutional neural network
  • WiFi Fingerprinting

Fingerprint Dive into the research topics of 'CNNLoc: Deep-Learning Based Indoor Localization with WiFi Fingerprinting'. Together they form a unique fingerprint.

  • Cite this

    Song, X., Fan, X., He, X., Xiang, C., Ye, Q., Huang, X., Fang, G., Chen, L. L., Qin, J., & Wang, Z. (2019). CNNLoc: Deep-Learning Based Indoor Localization with WiFi Fingerprinting. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 589-595). IEEE Xplore. https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00139