Mobile Sensor Data Collecting System Based on Smart Phone

  • Chen Zhen
  • Gao Qiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8351)


Smart phone applications are widely used with technological development of embedded sensors which bring the functions of sensing, communicating and computing to users. Large sets of sensor data (GPS, Bluetooth, compass, etc.) are exploited to the research of human behaviors and human social activities, but collecting a large scale dataset bears many problems. GPSTracker, a mobile application using embedded GPS module and Bluetooth module to collect mobile and interactive data with server/client architecture, is designed. This data collecting system based on smart phone of Android platform, which gives solutions to major problems of data collecting experiment in the aspects of usability, energy consumption, privacy and incentives for users to keep running it on their device for long-term. The performance analysis after a series of tests shows that GPSTracker is acceptable to users and has a high efficiency of data collection. It’s effective in controlling energy consumption and privacy.


Smart Phone Sensing Energy Consumption Privacy 


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  1. 1.
    Zhiwen, Y., Zhiyong, Y., Xingshe, Z.: Socially aware computing. Chinese Journal of Computers 35(1), 16–26 (2012)CrossRefGoogle Scholar
  2. 2.
    Chon, Y., Cha, H.: LifeMap: A smartphone-based context provider for location-based services. IEEE Pervasive Computing 10(2), 58–67 (2011)CrossRefGoogle Scholar
  3. 3.
    Yuyao, Y., Fang, Z., Haiyong, L., Ye, T., Xincan, L.: A geolocation-based social network model for mobile Internet. Journal of Computer Research and Development 48(z2), 307–313 (2011)Google Scholar
  4. 4.
    Hossmann, T., Efstratiou, C., Mascolo, C.: Collecting big datasets of human activity one checkin at a time. In: Proceedings of the 4th ACM International Workshop on Hot Topics in Planet-scale Measurement, pp. 15–20. ACM, New York (2012)CrossRefGoogle Scholar
  5. 5.
    Raento, M., Oulasvirta, A.: Designing for privacy and self-presentation in social awareness. Personal and Ubiquitous Computing 12(7), 527–542 (2008)CrossRefGoogle Scholar
  6. 6.
    Lu, H., Yang, J., Liu, Z., Lane, D., Choudhury, T., Campbell, A.T.: The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp. 71–84. ACM, New York (2010)CrossRefGoogle Scholar
  7. 7.
    Conzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(5), 779–782 (2008)CrossRefGoogle Scholar
  8. 8.
    Kortuem, G., Segall, Z.: Wearable communities: Augmenting social networks with wearable computers. IEEE Pervasive Computing 2(1), 71–78 (2003)CrossRefGoogle Scholar
  9. 9.
    Gonzalez, M.C., Barabasi, A.L.: Complex networks: From data to models. Nature Physics 3(4), 224–225 (2007)CrossRefGoogle Scholar
  10. 10.
    Eagle, N., Pentland, A., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences 106(36), 15274–15278 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chen Zhen
    • 1
  • Gao Qiang
    • 2
  1. 1.Sino-French Engineer SchoolBeihang UniversityBeijingP.R. China
  2. 2.Department of Electronics and Information EngineeringBeihang UniversityBeijingP.R. China

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