Distributed Feature Extraction for Event Identification

  • Teresa H. Ko
  • Nina M. Berry
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3295)


An important component of ubiquitous computing is the ability to quickly sense the dynamic environment to learn context awareness in real-time. To pervasively capture detailed information of movements, we present a decentralized algorithm for feature extraction within a wireless sensor network. By approaching this problem in a distributed manner, we are able to work within the real constraint of wireless battery power and its effects on processing and network communications. We describe a hardware platform developed for low-power ubiquitous wireless sensing and a distributed feature extraction methodology which is capable of providing more information to the user of events while reducing power consumption. We demonstrate how the collaboration between sensor nodes can provide a means of organizing large networks into information-based clusters.


Sensor Network Sensor Node Wireless Sensor Network Image Segmentation Image Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arora, A., Dutta, P., Bapat, S., Kulathumani, V., Zhang, H., Naik, V., Mittal, V., Cao, H.: Line in the sand: A wireless sensor network for target detection, classification, and tracking. Technical Reprort OSU-CISRC-12/03-TR71, Ohio State University (2003)Google Scholar
  2. 2.
    Berry, N., Davis, J., Ko, T., Kyker, R., Pate, R., Stinnet, R., Baker, J., Cusner, A., Van Dyke, C., Kyckelhahn, B., Stark, D.: Wireless sensor systems for sense/decide/act/communicate. Technical Report SAND2003-8803, Sandia National Laboratories (December 2003)Google Scholar
  3. 3.
    Chlipala, A., Hui, J., Tolle, G.: Deluge: Data dissemination in multi-hop sensor networks. Cs294-1 project report, U.C. Berkeley (December 2003)Google Scholar
  4. 4.
    Khan, S., Javed, O., Shah, M.: Tracking in uncalibrated cameras with overlapping field of view. In Performance Evaluation of Tracking and Surveillance (PETS) with CVPR (December 2001)Google Scholar
  5. 5.
    Kyker, R.: Hybrid emergency radiation detection: a wireless sensor network application for consequence management of a radiological release. In: SPIE’s Defense and Security Symposium: Digital Wireless Communications VI (April 2004)Google Scholar
  6. 6.
    Polastre, J., Szewcyk, R., Mainwaringa, A., Culler, D., Anderson, J.: Analysis of Wireless Sensor Networks for Habitat Monitoring. Kluwer Academic Pub., Dordrecht (2004)Google Scholar
  7. 7.
    Rahimi, A., Dunagan, B.: Sparse sensor networks. In: MIT Project Oxygen: Student Oxygen Workshop (2003)Google Scholar
  8. 8.
    Riley, R., Schott, B., Czarnaski, J., Thakkar, S.: Power-aware acoustic processing. In: 2nd International Workshop on Information Processing in Sensor Networks (April 2003)Google Scholar
  9. 9.
    Rowe, A., Rosenberg, C., Nourbakhsh, I.: A low cost embedded color vision system. In: Proceedings of IROS 2002 (2002)Google Scholar
  10. 10.
    Stathopoulos, T., Heidemann, J., Estrin, D.: A remote code update mechanism for wireless sensor networks. Technical Report 30, CENSGoogle Scholar
  11. 11.
    Viola, P., Jones, M.: Robust real-tiem object detection. In: Second International Workshop on Statistical and Computational Theories of Vision - Modeling, Learning, Computing, and Sampling (July 2003)Google Scholar
  12. 12.
    Want, R., Pering, T., Danneels, G., Kumar, M., Sundar, M., Light, J.: The personal server: Changing the way we think about ubiquitous computing. In: Ubiquicomp 2002: 4th International Conference on Ubiquitous Computing, September, October 2002, pp. 194–209 (2002)Google Scholar
  13. 13.
    Wilhelm, T., B(oe)hme, H.J., Gro(sz), H.M.: Looking closer. In: 1st European Conference on Mobile Robots (2003)Google Scholar
  14. 14.
    Yang, D.B., Gonzalez-Banos, H.H., Guibas, L.J.: Counting people in crowds with a real-time network of simple image sensors. In: International Conference of Computer Vision (October 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Teresa H. Ko
    • 1
  • Nina M. Berry
    • 1
  1. 1.Embedded Reasoning InstituteSandia National LaboratoriesLivermoreUSA

Personalised recommendations