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Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments
Joint Authors
Wang, Lin
Yang, Jing
Liu, Wenyuan
Source
International Journal of Distributed Sensor Networks
Issue
Vol. 2013, Issue - (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-21
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Telecommunications Engineering
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Wang, Lin& Yang, Jing& Liu, Wenyuan. 2013. Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments. International Journal of Distributed Sensor Networks،Vol. 2013, no. -, pp.1-11.
https://search.emarefa.net/detail/BIM-457045
Modern Language Association (MLA)
Wang, Lin…[et al.]. Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments. International Journal of Distributed Sensor Networks Vol. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-457045
American Medical Association (AMA)
Wang, Lin& Yang, Jing& Liu, Wenyuan. Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments. International Journal of Distributed Sensor Networks. 2013. Vol. 2013, no. -, pp.1-11.
https://search.emarefa.net/detail/BIM-457045
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-457045