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Distributed Feature Extraction for Event Identification

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Ambient Intelligence (EUSAI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3295))

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Abstract

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.

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References

  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. 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. 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. 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. 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. 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. Rahimi, A., Dunagan, B.: Sparse sensor networks. In: MIT Project Oxygen: Student Oxygen Workshop (2003)

    Google Scholar 

  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. Rowe, A., Rosenberg, C., Nourbakhsh, I.: A low cost embedded color vision system. In: Proceedings of IROS 2002 (2002)

    Google Scholar 

  10. Stathopoulos, T., Heidemann, J., Estrin, D.: A remote code update mechanism for wireless sensor networks. Technical Report 30, CENS

    Google Scholar 

  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. 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. Wilhelm, T., B(oe)hme, H.J., Gro(sz), H.M.: Looking closer. In: 1st European Conference on Mobile Robots (2003)

    Google Scholar 

  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 

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© 2004 Springer-Verlag Berlin Heidelberg

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Ko, T.H., Berry, N.M. (2004). Distributed Feature Extraction for Event Identification. In: Markopoulos, P., Eggen, B., Aarts, E., Crowley, J.L. (eds) Ambient Intelligence. EUSAI 2004. Lecture Notes in Computer Science, vol 3295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30473-9_14

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  • DOI: https://doi.org/10.1007/978-3-540-30473-9_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23721-1

  • Online ISBN: 978-3-540-30473-9

  • eBook Packages: Springer Book Archive

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