skip to main content
10.1145/3063955.3063989acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesacm-turcConference Proceedingsconference-collections
research-article

Crowdsourcing-based wifi fingerprint update for indoor localization

Published: 12 May 2017 Publication History

Abstract

Researches on indoor localization become more and more popular because human spend more life time indoors than outdoors. Among all of the present indoor localization technologies, WiFi fingerprint localization is the most widely used. The method of fingerprint is based on matching the current received signal strength with fingerprints stored in the database to get user's position. This method can get high precision with the simple operation, but it's a labor-intensive work to acquire the fingerprint database which costs much time and human resources. In this paper, we proposed a crowdsourcing method to build an auto-update fingerprint database using the data fed back by numerous users. First we detect the user's step sequence using inertial sensors built in smartphones. Then we establish a Hidden Markov Model (HMM) and propose a Ratio-based Map Matching(RMM) algorithm to match the step sequence with the real path in the map. After the successful match, we bind each fingerprint collected during a walk to its corresponding position, so the auto-update fingerprint database is generated. We did some experiments in a teaching building to evaluate our proposed method, and the results show the accuracy achieved by the method is related to the length of the step sequence. If the step sequence is long enough, the database we generated is very close to the manual measuring results.

References

[1]
H.-S. Ahn and W. Yu. Environmental-adaptive rssi-based indoor localization. Automation Science and Engineering, IEEE Transactions on, 6(4):626--633, 2009.
[2]
A. Barry, B. Fisher, and M. L. Chang. A long-duration study of user-trained 802.11 localization. In Mobile Entity Localization and Tracking in GPS-less Environnments, pages 197--212. Springer, 2009.
[3]
K. Chang and D. Han. Crowdsourcing-based radio map update automation for wi-fi positioning systems. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, pages 24--31. ACM, 2014.
[4]
D. Chen, L. Du, Z. Jiang, W. Xi, J. Han, K. Zhao, J. Zhao, Z. Wang, and R. Li. A fine-grained indoor localization using multidimensional wi-fi fingerprinting. In Parallel and Distributed Systems (ICPADS), 2014 20th IEEE International Conference on, pages 494--501. IEEE, 2014.
[5]
H.-C. Chen, T.-H. Lin, H. Kung, C.-K. Lin, and Y. Gwon. Determining rf angle of arrival using cots antenna arrays: a field evaluation. In MILITARY COMMUNICATIONS CONFERENCE, 2012-MILCOM 2012, pages 1--6. IEEE, 2012.
[6]
Y.-C. Chen, J.-R. Chiang, H.-h. Chu, P. Huang, and A. W. Tsui. Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics. In Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems, pages 118--125. ACM, 2005.
[7]
K. Chintalapudi, A. Padmanabha Iyer, and V. N. Padmanabhan. Indoor localization without the pain. In Proceedings of the sixteenth annual international conference on Mobile computing and networking, pages 173--184. ACM, 2010.
[8]
T. Gallagher, B. Li, A. G. Dempster, and C. Rizos. Database updating through user feedback in fingerprint-based wi-fi location systems. In Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 2010, pages 1--8. IEEE, 2010.
[9]
D. Gusenbauer, C. Isert, and J. Krösche. Self-contained indoor positioning on off-the-shelf mobile devices. In Indoor positioning and indoor navigation (IPIN), 2010 international conference on, pages 1--9. IEEE, 2010.
[10]
D. Hahnel, W. Burgard, D. Fox, K. Fishkin, and M. Philipose. Mapping and localization with rfid technology. In Robotics and Automation, 2004. Proceedings. ICRA'04. 2004 IEEE International Conference on, volume 1, pages 1015--1020. IEEE, 2004.
[11]
R. Hansen, R. Wind, C. S. Jensen, and B. Thomsen. Algorithmic strategies for adapting 802.11 location fingerprinting to environmental changes. In International Conference on Indoor Positioning and Indoor Navigation. Citeseer, 2010.
[12]
A. Hossain and W.-S. Soh. A comprehensive study of bluetooth signal parameters for localization. In Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. IEEE 18th International Symposium on, pages 1--5. IEEE, 2007.
[13]
Z. Jiang, J. Zhao, J. Han, S. Tang, J. Zhao, and W. Xi. Wi-fi fingerprint based indoor localization without indoor space measurement. In Mobile Ad-Hoc and Sensor Systems (MASS), 2013 IEEE 10th International Conference on, pages 384--392. IEEE, 2013.
[14]
J. Liu, B. Priyantha, T. Hart, H. S. Ramos, A. A. Loureiro, and Q. Wang. Energy efficient gps sensing with cloud offloading. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, pages 85--98. ACM, 2012.
[15]
L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil. Landmarc: indoor location sensing using active rfid. Wireless networks, 10(6):701--710, 2004.
[16]
J.-g. Park, B. Charrow, D. Curtis, J. Battat, E. Minkov, J. Hicks, S. Teller, and J. Ledlie. Growing an organic indoor location system. In Proceedings of the 8th international conference on Mobile systems, applications, and services, pages 271--284. ACM, 2010.
[17]
Y. Shu, C. Bo, G. Shen, C. Zhao, L. Li, and F. Zhao. Magicol: Indoor localization using pervasive magnetic field and opportunistic wifi sensing. IEEE Journal on Selected Areas in Communications, 33(7):1443--1457, 2015.
[18]
C. Wu, Z. Yang, and Y. Liu. Smartphones based crowdsourcing for indoor localization. Mobile Computing, IEEE Transactions on, 14(2):444--457, 2015.
[19]
C. Wu, Z. Yang, Y. Liu, and W. Xi. Will: Wireless indoor localization without site survey. IEEE Transactions on Parallel and Distributed Systems, 24(4):839--848, 2013.
[20]
C. Wu, Z. Yang, C. Xiao, C. Yang, Y. Liu, and M. Liu. Static power of mobile devices: Self-updating radio maps for wireless indoor localization. In Computer Communications (INFOCOM), 2015 IEEE Conference on, pages 2497--2505. IEEE, 2015.
[21]
J. Xiong and K. Jamieson. Arraytrack: a fine-grained indoor location system. In Presented as part of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13), pages 71--84, 2013.
[22]
Z. Yang and Y. Liu. Understanding node localizability of wireless ad hoc and sensor networks. Mobile Computing, IEEE Transactions on, 11(8):1249--1260, 2012.
[23]
J. Yin, Q. Yang, and L. Ni. Adaptive temporal radio maps for indoor location estimation. In Pervasive Computing and Communications, 2005. PerCom 2005. Third IEEE International Conference on, pages 85--94. IEEE, 2005.
[24]
J. Zhang, P. V. Orlik, Z. Sahinoglu, A. F. Molisch, and P. Kinney. Uwb systems for wireless sensor networks. Proceedings of the IEEE, 97(2):313--331, 2009.
[25]
B. Zhou, Q. Li, Q. Mao, W. Tu, and X. Zhang. Activity sequence-based indoor pedestrian localization using smartphones. Human-Machine Systems, IEEE Transactions on, 45(5):562--574, 2015.

Cited By

View all
  • (2024)Learning-Based WiFi Fingerprint Inpainting via Generative Adversarial Networks2024 33rd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN61486.2024.10637537(1-7)Online publication date: 29-Jul-2024
  • (2023)Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is DecisiveIEEE Journal of Indoor and Seamless Positioning and Navigation10.1109/JISPIN.2023.32994331(53-68)Online publication date: 2023
  • (2022)An Adaptive Update Algorithm for Fingerprint Database Based on Validity Detection2022 6th International Conference on Automation, Control and Robots (ICACR)10.1109/ICACR55854.2022.9935532(224-230)Online publication date: 23-Sep-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ACM TURC '17: Proceedings of the ACM Turing 50th Celebration Conference - China
May 2017
371 pages
ISBN:9781450348737
DOI:10.1145/3063955
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 May 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. HMM
  2. RMM
  3. crowdsourcing
  4. indoor localization
  5. step sequence
  6. wifi fingerprint

Qualifiers

  • Research-article

Conference

ACM TUR-C '17

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)3
Reflects downloads up to 26 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Learning-Based WiFi Fingerprint Inpainting via Generative Adversarial Networks2024 33rd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN61486.2024.10637537(1-7)Online publication date: 29-Jul-2024
  • (2023)Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is DecisiveIEEE Journal of Indoor and Seamless Positioning and Navigation10.1109/JISPIN.2023.32994331(53-68)Online publication date: 2023
  • (2022)An Adaptive Update Algorithm for Fingerprint Database Based on Validity Detection2022 6th International Conference on Automation, Control and Robots (ICACR)10.1109/ICACR55854.2022.9935532(224-230)Online publication date: 23-Sep-2022
  • (2022)MRILoc: Multiresolution Indoor Localization from crowdsourced samplesPervasive and Mobile Computing10.1016/j.pmcj.2022.10171987(101719)Online publication date: Dec-2022
  • (2020)Multi-Distance Function Trilateration over k-NN Fingerprinting for Indoor Positioning and Its EvaluationIEICE Transactions on Information and Systems10.1587/transinf.2019EDP7241E103.D:5(1055-1066)Online publication date: 1-May-2020
  • (2019)A Sparse Manifold Learning Approach to Robust Indoor Positioning Based on Wi-Fi RSS FingerprintingIEEE Access10.1109/ACCESS.2019.29406297(130791-130803)Online publication date: 2019
  • (2019)A Fingerprinting Trilateration Method FTM for Indoor Positioning and Its PerformanceHuman Aspects of IT for the Aged Population. Social Media, Games and Assistive Environments10.1007/978-3-030-22015-0_26(326-335)Online publication date: 8-Jun-2019
  • (2019)Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environmentTransactions on Emerging Telecommunications Technologies10.1002/ett.370530:9Online publication date: 12-Sep-2019
  • (2018)Cooperative Target Tracking and Signal Propagation Learning Using Mobile SensorsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649462:3(1-21)Online publication date: 18-Sep-2018

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media