Skip to main content
SpringerLink
Log in

Radio-Map Search Algorithm Based on Steepest Descent Principle

  • Conference paper
  • First Online:
Book cover Ad Hoc Networks (ADHOCNETS 2018)

Included in the following conference series:

  • 494 Accesses

Abstract

For most of the Ad-Hoc systems, position information is very important. Indoor scenario is a blind area of Global Navigation Satellite System (GNSS) service, which affects the application of Ad-Hoc technology. Fingerprint positioning technology is one of the most popular indoor localization methods. Searching strategy is one of the key techniques of fingerprint positioning. Because the data amount of the radio-map, which is used as the database of the system, is very big. Currently, the main accelerating measure of radio-map searching is clustering. But clustering brings some problems to the system, such as jittering and jamming. This paper proposes a novel radio-map searching strategy. Based on the steepest descent principle, the searching order is changed in the proposed method, compared with traditional clustering-positioning strategy. Thus, the radio-map is used in one piece, which is different from the traditional clustering-matching strategy. Simulations and experiments verified that the positioning accuracy of the proposal is better than that of the traditional method.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zou, G., Ma, L., Zhang, Z., Mo, Y.: An indoor positioning algorithm using joint information entropy based on WLAN fingerprint. In: Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Hefei, China (2014)

    Google Scholar 

  2. Liu, X., Zhang, S., Zhao, Q., Lin, X.: A real-time algorithm for fingerprint localization based on clustering and spatial diversity. In: International Congress on Ultra-Modern Telecommunications and Control Systems, Moscow, Russa (2010)

    Google Scholar 

  3. Li, K., Bigham, J., Tokarchuk, L., Bodanese, E.L.: A probabilistic approach to outdoor localization using clustering and principal component transformations. In: 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Italian, Sardinia (2013)

    Google Scholar 

  4. Feng, C., Au, W.S.A., Valaee, S., Tan, Z.: Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans. Mob. Comput. 11(12), 1983–1993 (2012)

    Article  Google Scholar 

  5. Lee, C.W., Lin, T.N., Fang, S.H., Chou, Y.C.: A novel clustering-based approach of indoor location fingerprinting. In: 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), London, England (2013)

    Google Scholar 

  6. Premchaisawatt, S., Ruangchaijatupon, N.: Enhancing indoor positioning based on partitioning cascade machine learning models. In: 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Nakhon Ratchasima (2014)

    Google Scholar 

  7. Dousse, O., Eberle, J., Mertens, M.: Place learning via direct WiFi fingerprint clustering. In: 2012 IEEE 13th International Conference on Mobile Data Management, Bengaluru, Karnataka (2012)

    Google Scholar 

  8. Lin, H., Chen, L.: An optimized fingerprint positioning algorithm for underground garage environment. In: 2016 International Conference on Information Networking (ICOIN), Kota Kinabalu (2016)

    Google Scholar 

  9. Zhang, W., Hua, X., Yu, K., Qiu, W., Zhang, S.: Domain clustering based WiFi indoor positioning algorithm. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares (2016)

    Google Scholar 

  10. Zhong, W., Yu, J.: WLAN floor location method based on hierarchical clustering. In: 2015 3rd International Conference on Computer and Computing Science (COMCOMS), Hanoi, Vietnam (2015)

    Google Scholar 

  11. Lin, Y.T., Yang, Y.H., Fang, S.H.: A case study of indoor positioning in an unmodified factory environment. In: 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea (2014)

    Google Scholar 

  12. Cai, D.: A retail application based on indoor location with grid estimations. In: 2014 International Conference on Computer, Information and Telecommunication Systems (CITS), Jeju, Korea (2014)

    Google Scholar 

Download references

Acknowledgment

This research is supported by the Fundamental Research Funds for the Central Universities DUT16RC (3)100, And partly supported by the National Natural Science Foundation of China (NO. 61701072).

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Linggong str. 2, Dalian, 116024, China

    Deyue Zou

  2. Academy of Opto-Electronics, Chinese Academy of Sciences, Dengzhuang South str. 9, Beijing, 100089, China

    Yuwei Shi

  3. Communication Research Center, Harbin Institute of Technology, Yikuang str. 2, Harbin, 150080, China

    Shuai Han

Authors
  1. Deyue Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuwei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuai Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deyue Zou .

Editor information

Editors and Affiliations

  1. National Mobile Communications Research Laboratory, Southeast University, Nanjing, China

    Jun Zheng

  2. College of Science and Engineering, James Cook University, Cairns, QLD, Australia

    Wei Xiang

  3. IUT, University of Haute Alsace, Colmar, France

    Pascal Lorenz

  4. Department of Electrical and Computer Engineering, Auburn University, AUBURN, AL, USA

    Shiwen Mao

  5. National Mobile Communications Research Laboratory, Southeast University, Nanjing, China

    Feng Yan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zou, D., Shi, Y., Han, S. (2019). Radio-Map Search Algorithm Based on Steepest Descent Principle. In: Zheng, J., Xiang, W., Lorenz, P., Mao, S., Yan, F. (eds) Ad Hoc Networks. ADHOCNETS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-05888-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05888-3_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05887-6

  • Online ISBN: 978-3-030-05888-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation