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Wi-Fi Floor Localization in an Unsupervised Manner

  • Liangliang LinEmail author
  • Wei Shi
  • Muhammad Asim
  • Hui Zhang
  • Shuting Hu
  • Jizhong Zhao
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 303)

Abstract

In recent decades, with the development of computer, indoor positioning applications have been developed rapidly. GPS has become one of the standards for outdoor positioning. However, there are great conditions for the use of GPS, GPS cannot be used indoors. At the same time, the indoor positioning scene has a great application prospect, through the use of indoor accessible signals (such as Wi-Fi, ZigBee, Bluetooth, UWB, etc.), according to the indoor environment and application, can be created based on the indoor positioning system. In the indoor positioning, there are two challenges, first of all, floor positioning, if the building has more than two layers, the second is planar positioning.

This paper solves the problem of floor positioning, and floor positioning based on Wi-Fi unsupervised recognition has attracted wide attention because it can get positioning results at a lower cost. In this paper, we try unsupervised indoor positioning methods, using only Wi-Fi crowdsourcing data. We get four months of data from seven-story buildings, by scanning the router’s information. The application of neural network model can achieve unsupervised indoor positioning.

This clustering model aggregates all signals from the same floor into one class, and we use convolution neural networks, descending dimension feature extraction functions. The experiments show our solution obtains very high precision clustering results, so it can be summed up in this sense that the Wi-Fi crowdsourcing data can be used to locate in some way as the future direction of indoor positioning development.

Keywords

Indoor localization CNN K-means Floor localization 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Liangliang Lin
    • 1
    • 2
    Email author
  • Wei Shi
    • 1
  • Muhammad Asim
    • 1
  • Hui Zhang
    • 1
  • Shuting Hu
    • 3
  • Jizhong Zhao
    • 1
  1. 1.School of Computer Science and Technology, Department of TelecommunicationsXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  2. 2.Information DepartmentXi’an Conservatory of MusicXi’anPeople’s Republic of China
  3. 3.Qinghai UniversityXiningPeople’s Republic of China

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