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)


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.


Indoor localization CNN K-means Floor localization 


  1. 1.
    Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using wikipedia. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 787–788. ACM (2007)Google Scholar
  2. 2.
    Fodeh, S., Punch, B., Tan, P.-N.: On ontology-driven document clustering using core semantic features. Knowl. Inf. Syst. 28(2), 395–421 (2011)CrossRefGoogle Scholar
  3. 3.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. JAsIs 41(6), 391–407 (1990)CrossRefGoogle Scholar
  4. 4.
    Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. Adv. Neural. Inf. Process. Syst. 2, 849–856 (2002)Google Scholar
  5. 5.
    He, X., Niyogi, P.: Locality preserving projections. In: Neural Information Processing Systems, vol. 16, pp. 153–160. MIT (2004)Google Scholar
  6. 6.
    Yin, J., Wang, J.: A dirichlet multinomial mixture model-based approach for short text clustering. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 233–242. ACM (2014)Google Scholar
  7. 7.
    Tang, J., Wang, X., Gao, H., Hu, X., Liu, H.: Enriching short text representation in microblog for clustering. Front. Comput. Sci. 6(1), 88–101 (2012)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394 (2010)Google Scholar
  10. 10.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  11. 11.
    Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the Empiricial Methods in Natural Language Processing, p. 12 (2014)Google Scholar
  12. 12.
    Mikolov, T., Chen, K., Corrado, G., Dean, J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013)
  13. 13.
    Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., et al.: Skip-thought vectors. In: Advances in Neural Information Processing Systems, pp. 3276–3284 (2015)Google Scholar
  14. 14.
    Wang, X., Gupta, A. Unsupervised learning of visual representations using videos, arXiv preprint arXiv:1505.00687. (2015)
  15. 15.
    Jiaming, X., Bo, X., Wang, P., Zheng, S., Tian, G., Zhao, J.: Self-taught convolutional neural networks for short text clustering. Neural Netw. 88, 22–31 (2017)CrossRefGoogle Scholar
  16. 16.
    Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (2014)Google Scholar
  17. 17.
    Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2014)Google Scholar
  19. 19.
    Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Trans. Knowl. Data Eng. 17(12), 1624–1637 (2005)CrossRefGoogle Scholar
  20. 20.
    Huang, P., Huang, Y., Wang, W., Wang, L.: Deep embedding network for clustering. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1532–1537. IEEE (2014)Google Scholar
  21. 21.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial optimization: algorithms and complexity. Courier Corporation (1998)Google Scholar
  22. 22.
    Chen, W.-Y., Song, Y., Bai, H., Lin, C.-J., Chang, E.Y.: Parallel spectral clustering in distributed systems. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 568–586 (2011)CrossRefGoogle Scholar

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

Personalised recommendations