Analysis of XJTLUIndoorLoc Multivariate Dataset for DNN-Based Indoor Localization

Table of Contents

Abstract

Through the last year's SURF project, we built a multivariate dataset—i.e., XJTLUIndoorLoc—for indoor localization and trajectory estimation based on Wi-Fi received signal strength (RSS) and geomagnetic field, which covers the 4th and the 5th floor of the IBSS building and includes measurement data at 969 reference points. In this project, we are to carry out a systematic analysis of XJTLUIndoorLoc dataset to investigate the issues of the dependency of measurement data on mobile devices and the lack of device orientation information for geomagnetic field in deep neural network (DNN)-based indoor localization and trajectory estimation.

Research questions

XJTLUIndoorLoc dataset was built to support large-scale indoor localization and trajectory estimation based on fingerprinting and/or time-series modelling using DNNs. The key research questions of this project for XJTLUIndoorLoc dataset are as follows:

  • What is the impact of mobile devices (e.g., different brands and models of smartphones) on the measured RSS and geomagnetic field intensity and resulting indoor localization/trajectory estimation performance and how to address it in DNN-based indoor localization/trajectory estimation?
  • How to handle the device orientation information in the measurement of geomagnetic field intensity?

People

Research Assistants

Participants

Grants

Duration

  • Jun./2019–Aug./2019 (10 weeks)

Meetings

  • 10/07/2019: 4th meeting, 10-12 AM, IR515
    • Group discussions
  • 03/07/2019: 3rd meeting, 10-12 AM, EE505
  • 26/06/2019: 2nd meeting, 4-5 PM, IR515
  • 19/06/2018: Kick-off meeting, 10-11 AM, EE505
  • 06/05/2018: Preparation meeting, 5-6 PM, EE505

Outcomes

References

Trajectory estimation

  1. Ho Jun Jang, Jae Min Shin, and Lynn Choi, "Geomagnetic field based indoor localization using recurrent neural networks," Proc. GLOBECOM 2017, pp. 1-6, Dec. 2017. (DOI)
  2. Wei Zhang, Kan Liu, Weidong Zhang, Youmei Zhang, and Jason Gu, "Deep neural networks for wireless localization in indoor and outdoor environments," Neurocomputing, vol. 194, pp. 279-287, 2016. (DOI)

Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks

  1. Christopher Olah, Understanding LSTM networks, Accessed May 22, 2018.
  2. Yuan Lukito and Antonius Rachmat Chrismanto, "Recurrent neural networks model for WiFi-based indoor positioning system," Proc. of 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems, Yogyakarta, Indonesia, Nov. 2017. (DOI)

Convolutional neural networks (CNNs) for time series data (e.g., audio signal)

  1. Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton, "Speech recognition with deep recurrent neural networks," arXiv:1303.5778 [cs.NE], Mar. 2013. (arXiv)
  2. Grégoire Montavon, "Deep learning for spoken language identification," Proc. NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, 2009.

Wi-Fi fingerprinting and deep learning

  1. Kyeong Soo Kim, Ruihao Wang, Zhenghang Zhong, Zikun Tan, Haowei Song, Jaehoon Cha, and Sanghyuk Lee, "Large-scale location-aware services in access: Hierarchical building/floor classification and location estimation using Wi-Fi fingerprinting based on deep neural networks," (Extended version of the FOAN 2017 paper), Fiber and Integrated Optics, vol. 37, no. 5, pp. 277-289, Apr. 27, 2018. (DOI)
  2. Kyeong Soo Kim, Sanghyuk Lee, and Kaizhu Huang "A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting," Big Data Analytics, vol. 3, no. 4., pp. 1017, Apr. 19, 2018. (DOI) (arXiv)
  3. Kyeong Soo Kim, Ruihao Wang, Zhenghang Zhong, Zikun Tan, Haowei Song, Jaehoon Cha, and Sanghyuk Lee, "Large-scale location-aware services in access: Hierarchical building/floor classification and location estimation using Wi-Fi fingerprinting based on deep neural networks," Proc. of FOAN 2017, Munich, Germany, Nov. 7, 2017. (arXiv)
  4. Mai Ibrahim, Marwan Torki, and Mustafa ElNainay, "CNN based indoor localization using RSS time-series," Proc. of ISCC 2018, pp. 1-6, Natal, Brazil, Jun. 2018. (ResearchGate)
  5. Paramvir Bahl and Venkata N. Padmanabhan, "RADAR: An In-Building RF-based User Location and Tracking System," Proc. INFOCOM 2000, pp. 1-10, 2000.
  6. Michal Nowicki and Jan Wietrzykowski, "Low-effort place recognition with WiFi fingerprints using deep learning," arXiv:1611.02049 [cs.RO], Apr. 2017. (arXiv)
  7. Keras: A high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano; now part of TensorFlow distribution.
  8. Shixiong Xia, Yi Liu, Guan Yuan, Mingjun Zhu, and Zhaohui Wang, "Indoor Fingerprint Positioning Based on Wi-Fi: An Overview," ISPRS Int. J. Geo-Inf, vol. 6, no. 5, 135, pp. 1-25, 2017.
  9. John S. Breese, David Heckerman, and Carl Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," Tech. Report, MSR-TR-98-12, Microsoft, May 1, 1998.
  10. CS246 Mining Massive Data Sets Winter 2017, Chapter 9. Recommendation systems, Stanford, 2017.

Fingerprint datasets

  1. J. Torres-Sospedra et al., "UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems," Proc. IPIN 2014, pp. 261-270, Busan, Korea, Oct. 2014. (DOI)
  2. J. Torres-Sospedra et al., "UJIIndoorLoc-Mag: A new database for magnetic field-based localization problems," Proc. IPIN 2015, pp. 1-10, Banff, Alberta, Canada, Oct. 2015. (DOI)
  3. UCI Machine Learning Repository, UJIIndoorLoc Data Set (fingerprint database for the above two papers and many others).
  4. E. S. Lohan et al., "Wi-Fi crowdsourced fingerprinting dataset for indoor positioning," Data, vol. 2, no. 4, article no. 32, pp. 1-16, 2017.

GitHub repositories for Python codes and fingerprint data

Android programming

Related Projects

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