Skip to main content
SpringerLink
Log in

Towards Floor Identification and Pinpointing Position: A Multistory Localization Model with WiFi Fingerprint

  • Regular Papers
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

The era of Internet of Things (IoT) has stimulated the diversification of wireless applications, and a pragmatic way is to adopt and leverage WiFi to pinpoint the position of a mobile device. However, there still exist significant challenges in this field, such as heterogeneous crowd-sourced data distribution, external scene interference, etc. We focus on indoor WiFi fingerprint localization in multistory buildings. To confine the search scope to a specific floor, we propose a novel floor identification module. In this module we construct a WiFi fingerprint graph representation to fully explore the correlations of reference points (RP). Furthermore, a fingerprint graph attention mechanism is introduced to capture the importance of adjoining fingerprints for a more accurate floor identification. In addition, a two-panel fingerprint homogeneity graph is adopted to gauge the resemblance of localization fingerprints, and the estimated 2-D location is predicted by the integration of panel results. By comprehensively analyzing the fingerprint attributes of a crowd-sourced database, we have conducted experiments to demonstrate the localization algorithm’s performance. Compared with other algorithms, the results show that the proposed method can achieve the best performance in floor identification, reaching 96.93%; In the aspect of 2-D geometric positioning, the proposed method also has better performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. T. H. Fang, Y. Kim, S. G. Park, K. Seo, and S. H. Park, “GPS and eLoran integrated navigation for marine applications using augmented measurement equation based on range domain,” International Journal of Control, Automation, and Systems, vol. 18, pp. 2349–2359, 2020.

    Article  Google Scholar 

  2. V. Havyarimana, Z. Xiao, A. Sibomana, D. Wu, and J. Bai, “A fusion framework based on sparse Gaussian-Wigner prediction for vehicle localization using GDOP of GPS satellites,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 2, pp. 680–689, 2020.

    Article  Google Scholar 

  3. X. Chen and Y. Jia, “Indoor localization for mobile robots using lampshade corners as landmarks: Visual system calibration, feature extraction and experiments,” International Journal of Control, Automation, and Systems, vol. 12, no. 6, pp. 1313–1322, 2014.

    Article  Google Scholar 

  4. J.-H. Kim, J.-E. Lee, J.-H. Lee, and G.-T. Park, “Motion-based identification of multiple mobile robots using trajectory analysis in a well-configured environment with distributed vision sensors,” International Journal of Control, Automation, and Systems, vol. 10, no. 4, pp. 787–796, 2012.

    Article  Google Scholar 

  5. M. S. Lee, H. Ju, and C. G. Park, “Map assisted PDR/Wi-Fi fusion for indoor positioning using smartphone,” International Journal of Control, Automation, and Systems, vol. 15, no. 2, pp. 627–639, 2017.

    Article  Google Scholar 

  6. G. Park, B. Lee, D. G. Kim, Y. J. Lee, and S. Sung, “Design and performance validation of integrated navigation system based on geometric range measurements and GIS map for urban aerial navigation,” International Journal of Control, Automation, and Systems, vol. 18, no. 10, pp. 2509–2521, 2020.

    Article  Google Scholar 

  7. D. Ciuonzo and P. S. Rossi, Eds., Data Fusion in Wireless Sensor Networks: A Statistical Signal Processing Perspective, ser. Control, Robotics & Sensors, Institution of Engineering and Technology, 2019.

  8. S. H. Javadi, H. Moosaei, and D. Ciuonzo, “Learning wireless sensor networks for source localization,” Sensors, vol. 19, no. 3, p. 635, 2019.

    Article  Google Scholar 

  9. Y. Zhao, W. Wong, T. Feng, and H. K. Garg, “Efficient and scalable calibration-free indoor positioning using crowdsourced data,” IEEE Internet of Things Journal, vol. 7, no. 1, pp. 160–175, 2020.

    Article  Google Scholar 

  10. R. Liu, S. H. Marakkalage, M. Padmal, T. Shaganan, C. Yuen, Y. L. Guan, and U. Tan, “Collaborative SLAM based on WiFi fingerprint similarity and motion information,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1826–1840, 2020.

    Article  Google Scholar 

  11. G. Caso, L. De Nardis, F. Lemic, V. Handziski, A. Wolisz, and M. D. Benedetto, “ViFi: Virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model,” IEEE Transactions on Mobile Computing, vol. 19, no. 6, pp. 1478–1491, 2020.

    Article  Google Scholar 

  12. S. Dai, L. He, and X. Zhang, “Autonomous WiFi fingerprinting for indoor localization,” Proc. of ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), pp. 141–150, 2020.

  13. M. Abbas, M. Elhamshary, H. Rizk, M. Torki, and M. Youssef, “WiDeep: WiFi-based accurate and robust indoor localization system using deep learning,” Proc. of IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–10, 2019.

  14. L. Li, X. Guo, N. Ansari, and H. Li, “A hybrid fingerprint quality evaluation model for WiFi localization,” IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9829–9840, 2019.

    Article  Google Scholar 

  15. X. Guo, N. R. Elikplim, N. Ansari, L. Li, and L. Wang, “Robust WiFi localization by fusing derivative fingerprints of RSS and multiple classifiers,” IEEE Transactions on Industrial Informatics, vol. 16, no. 5, pp. 3177–3186, 2020.

    Article  Google Scholar 

  16. G. Huang, Z. Hu, J. Wu, H. Xiao, and F. Zhang, “WiFi and vision integrated fingerprint for smartphone-based self-localization in public indoor scenes,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6748–6761, 2020.

    Article  Google Scholar 

  17. S. P. Rana, J. Prieto, M. Dey, S. Dudley, and J. M. Corchado, “A self regulating and crowdsourced indoor positioning system through Wi-Fi fingerprinting for multi storey building,” Sensors, vol. 18, no. 11, p. 3766, 2018.

    Article  Google Scholar 

  18. L. Han, L. Jiang, Q. Kong, J. Wang, A. Zhang, and S. Song, “Indoor localization within multi-story buildings using MAC and RSSI fingerprint vectors,” Sensors, vol. 19, no. 11, p. 2433, 2019.

    Article  Google Scholar 

  19. B. Wu and C. Jen, “Particle-filter-based radio localization for mobile robots in the environments with low-density WLAN APS,” IEEE Transactions on Industrial Electronics, vol. 61, no. 12, pp. 6860–6870, 2014.

    Article  Google Scholar 

  20. H. Zou, Ming Jin, H. Jiang, L. Xie, and C. Spanos, “WinIPS: WiFi-based non-intrusive IPS for online radio map construction,” Proc. of IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1081–1082, 2016.

  21. H. Zou, Y. Zhou, J. Yang, and C. J. Spanos, “Unsupervised WiFi-enabled IoT device-user association for personalized location-based service,” IEEE Internet of Things Journal, vol. 6, no. 1, pp. 1238–1245, 2019.

    Article  Google Scholar 

  22. R. C. Luo and T. J. Hsiao, “Dynamic wireless indoor localization incorporating with an autonomous mobile robot based on an adaptive signal model fingerprinting approach,” IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 1940–1951, 2019.

    Article  Google Scholar 

  23. Ó. Belmonte-Fernández, R. Montoliu, J. Torres-Sospedra, E. Sansano-Sansano, and D. Chia-Aguilar, “A radiosity-based method to avoid calibration for indoor positioning systems,” Expert Systems with Applications, vol. 105, pp. 89–101, 2018.

    Article  Google Scholar 

  24. M. Liu, R. Chen, D. Li, Y. Chen, G. Guo, Z. Cao, and Y. Pan, “Scene recognition for indoor localization using a multi-sensor fusion approach,” Sensors, vol. 17, no. 12, p. 2847, 2017.

    Article  Google Scholar 

  25. Y. Zhuang, J. Yang, L. Qi, Y. Li, Y. Cao, and N. El-Sheimy, “A pervasive integration platform of low-cost mems sensors and wireless signals for indoor localization,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4616–4631, 2017.

    Article  Google Scholar 

  26. E. S. Lohan, J. Torres-Sospedra, H. Leppäkoski, P. Richter, Z. Peng, and J. Huerta, “Wi-Fi crowdsourced fingerprinting dataset for indoor positioning,” Data, vol. 2, no. 4, p. 32, 2017.

    Article  Google Scholar 

  27. A. R. Linero, “A review of tree-based Bayesian methods,” Communications for Statistical Applications and Methods, vol. 24, no. 6, pp. 543–559, 2017.

    Article  Google Scholar 

  28. J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, “A comparison of random forest variable selection methods for classification prediction modeling,” Expert Systems with Applications, vol. 134, pp. 93–101, 2019.

    Article  Google Scholar 

  29. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems, pp. 5998–6008, 2017.

  30. Y. Zhuang, Z. Syed, J. Georgy, and N. El-Sheimy, “Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing,” Pervasive and Mobile Computing, vol. 18, pp. 118–136, 2015.

    Article  Google Scholar 

  31. A. Razavi, M. Valkama, and E. Lohan, “K-means fingerprint clustering for low-complexity floor estimation in indoor mobile localization,” Proc. of IEEE Globecom Workshops (GC Wkshps), pp. 1–7, 2015.

  32. A. Cramariuc, H. Huttunen, and E. S. Lohan, “Clustering benefits in mobile-centric WiFi positioning in multi-floor buildings,” Proc. of International Conference on Localization and GNSS (ICL-GNSS), pp. 1–6, 2016.

  33. S. Shrestha, J. Talvitie, and E. S. Lohan, “Deconvolution-based indoor localization with WLAN signals and unknown access point locations,” Proc. of International Conference on Localization and GNSS (ICL-GNSS), pp. 1–6, 2013.

  34. J. Torres-Sospedra, R. Montoliu, S. Trilles, Ó. Belmonte, and J. Huerta, “Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems,” Expert Systems with Applications, vol. 42, no. 23, pp. 9263–9278, 2015.

    Article  Google Scholar 

  35. A. Moreira, M. J. Nicolau, F. Meneses, and A. Costa, “Wi-Fi fingerprinting in the real world-RTLS@UM at the EvAAL competition,” Proc. of International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–10, 2015.

  36. J. Torres-Sospedra, A. Moreira, S. Knauth, R. Berkvens, R. Montoliu, O. Belmonte, S. Trilles, M. Joao Nicolau, F. Meneses, A. Costa, A. Koukofikis, M. Weyn, and H. Peremans, “A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL-ETRI competition,” Journal of Ambient Intelligence and Smart Environments, vol. 9, no. 2, pp. 263–279, 2017.

    Article  Google Scholar 

  37. J. Machaj, P. Brida, and R. Piché, “Rank based fingerprinting algorithm for indoor positioning,” Proc. of International Conference on Indoor Positioning and Indoor Navigation, pp. 1–6, 2011.

  38. H. Leppäkoski, S. Tikkinen, and J. Takala, “Optimizing radio map for WLAN fingerprinting,” Proc. of Ubiquitous Positioning Indoor Navigation and Location Based Service, pp. 1–8, 2010.

  39. R. Piché, “Robust estimation of a reception region from location fingerprints,” Proc. of International Conference on Localization and GNSS (ICL-GNSS), pp. 31–35, 2011.

  40. M. Raitoharju, M. Dashti, S. Ali-Löytty, and R. Piche, “Positioning with multilevel coverage area models,” Proc. of International Conference on Indoor Positioning and Indoor Navigation, IPIN, Sydney, Australia, pp. 1–6, 2012.

  41. “Lightgbm’s documentation,” https://lightgbm.readthe-docs.io/en/latest/.

  42. S. Barnwal and W. Peng, “Crowdsensing-based WiFi indoor localization using feed-forward multilayer perceptron regressor,” Proc. of International Conference on Computational Intelligence in Data Science (ICCIDS), pp. 1–6, 2019.

  43. J. Torres-Sospedra, R. Montoliu, A. Martínez-Usó, J. P. Avariento, T. J. Arnau, M. Benedito-Bordonau, and J. Huerta, “UJIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems,” Proc. of International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 261–270, 2014.

Download references

Author information

Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Xing Zhang, Wei Sun, Min Xue & Chenjun Tang

  2. School of Architecture and Art, Central South University, China, Changsha

    Jin Zheng

  3. School of Computing, National University of Singapore, Singapore, Singapore

    Roger Zimmermann

Authors
  1. Xing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Min Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chenjun Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Roger Zimmermann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Wei Sun, Jin Zheng or Min Xue.

Additional information

Xing Zhang received his B.Eng. degree from Hunan University of Science and Technology, and an M.E. degree in mechanical engineering from Hunan University, Changsha, China, in 2013 and 2016, respectively. He is currently working towards a Ph.D. degree at the college of Electrical and Information Engineering, Hunan University. His research interests include indoor localization, navigation and evolutionary computation.

Wei Sun received his B.S., M.S., and Ph.D. degrees from the Department of Automation Engineering, Hunan University, China, in 1997, 1999 and 2003, respectively. He is now working as a Professor at the College of Electrical and Information Engineering, Hunan University. His areas of interests are computer vision and robotics, neural networks, and intelligent control.

Jin Zheng received her B.S., M.S., and Ph.D. degrees from the School of Architecture, Hunan University, Changsha, China, in 1998, 2001, and 2019, respectively. She is currently a Lecturer with the School of Architecture and Art, Central South University, Changsha. Her current research interests include building intelligence and intelligent information processing.

Min Xue has been pursuing a Ph.D. degree in control science and engineering since 2017, at Hunan University, Changsha, China. She is also with the Key Laboratory of Intelligent Robot Technology in Electronic manufacturing, Hunan, China. From 2019 to 2021, she is a joint Ph.D. student at the National University of Singapore. Her current research interests include indoor localization, navigation, and intelligent robot.

Chenjun Tang received his B.Eng. degree from Hunan Normal University, and an M.E. degree in control science and engineering from Hunan University, Changsha, China, in 2017 and 2020, respectively. He is currently working towards a Ph.D. degree at the college of Electrical and Information Engineering, Hunan University. His research interests include indoor localization, navigation, and evolutionary computation.

Roger Zimmermann received his M.S. and Ph.D. degrees from the University of Southern California, Los Angeles, CA, USA, in 1994 and 1998, respectively. He is currently an Associate Professor with the Department of Computer Science, National University of Singapore (NUS). He is also a Deputy Director with the Smart Systems Institute (SSI), and previously codirected the Centre of Social Media Innovations for Communities at NUS. He has coauthored a book, seven patents, and more than 200 conference publications, journal articles, and book chapters. His research interests include streaming media architectures, distributed systems, mobile and geo-referenced video management, collaborative environments, spatio-temporal information management, and mobile location-based services. He is a distinguished member of the ACM and a senior member of the IEEE. Further information can be found at http://www.comp.nus.edu.sg/cs/people/rogerz/.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Sun, W., Zheng, J. et al. Towards Floor Identification and Pinpointing Position: A Multistory Localization Model with WiFi Fingerprint. Int. J. Control Autom. Syst. 20, 1484–1499 (2022). https://doi.org/10.1007/s12555-020-0978-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-020-0978-4

Keywords

Search

Navigation