IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v9y2013i10p246916.html
   My bibliography  Save this article

Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments

Author

Listed:
  • Lin Wang
  • Jing Yang
  • Wenyuan Liu

Abstract

Neighbor discovery for moving individual is considered an important technology submitting to location-based service (LBS), which includes such things as recruitment flow of information, logical localization, and health monitoring. Based on the tradeoff between universality and accuracy of neighbor discovery, we propose the environmental characteristics participatory extraction method benefiting to mobile individual discovery. First, we fuse lightweight accelerometer, light sensors, and microphone collaboratively. Furthermore, support vector machine (SVM), Tanimoto coefficient, and Manhattan distance are used to calculate three kinds of fingerprint similarity, respectively, and then the principal component analysis based method reduces data dimension in order to obtain neighbor similarity rank. Finally, the experiment data are collected by 25 volunteers, and trace-driven simulations show that Euclidean distance error is below 4.69 and the convergence time is within 0.75 s.

Suggested Citation

  • Lin Wang & Jing Yang & Wenyuan Liu, 2013. "Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments," International Journal of Distributed Sensor Networks, , vol. 9(10), pages 246916-2469, October.
    • Handle: RePEc:sae:intdis:v:9:y:2013:i:10:p:246916
    DOI: 10.1155/2013/246916
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1155/2013/246916
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/246916?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:9:y:2013:i:10:p:246916. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.