nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Wi-Fi Floor Localization in an Unsupervised Manner

verfasst von : Liangliang Lin, Wei Shi, Muhammad Asim, Hui Zhang, Shuting Hu, Jizhong Zhao

Erschienen in: Broadband Communications, Networks, and Systems

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Convex Optimization Algorithm for Wireless Localization by Using Hybrid RSS and AOA Measurements

Nächstes Kapitel Virtual Network Embedding Algorithm Based on Multi-objective Particle Swarm Optimization of Pareto Entropy

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)

Fodeh, S., Punch, B., Tan, P.-N.: On ontology-driven document clustering using core semantic features. Knowl. Inf. Syst. 28(2), 395–421 (2011)CrossRef

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)CrossRef

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)

He, X., Niyogi, P.: Locality preserving projections. In: Neural Information Processing Systems, vol. 16, pp. 153–160. MIT (2004)

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)

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)MathSciNetCrossRef

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRef

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)

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)

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)

12.

Mikolov, T., Chen, K., Corrado, G., Dean, J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013)

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)

14.

Wang, X., Gupta, A. Unsupervised learning of visual representations using videos, arXiv preprint arXiv:1505.00687. (2015)

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)CrossRef

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)

17.

Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH

18.

Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2014)

19.

Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Trans. Knowl. Data Eng. 17(12), 1624–1637 (2005)CrossRef

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)

21.

Papadimitriou, C.H., Steiglitz, K.: Combinatorial optimization: algorithms and complexity. Courier Corporation (1998)

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)CrossRef

Titel: Wi-Fi Floor Localization in an Unsupervised Manner
verfasst von: Liangliang Lin
Wei Shi
Muhammad Asim
Hui Zhang
Shuting Hu
Jizhong Zhao
Verlag: Springer International Publishing
Buch: Broadband Communications, Networks, and Systems
Print ISBN: 978-3-030-36441-0

Electronic ISBN: 978-3-030-36442-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-36442-7_4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"