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Data Fusion in Sensor Networks

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Synonyms

Distributed sensor fusion

Definition

Data fusion in sensor networks is defined as the set of algorithms, processes, and protocols that combine data from multiple sensors. The goal may be to extract information not readily apparent in an individual sensor’s data, improve the quality of information compared to that provided by any individual data, or improve the operation of the network by optimizing usage of its resources.

For instance, the output of a magnetic sensor and an audio sensor may be combined to detect a vehicle (new information), outputs of multiple vibration sensors may be combined to increase the signal to noise ratio (improving quality), or a passive infrared sensor may be combined with a camera in a people detection network to reduce the frame-rate of the camera for conserving energy (improving operation).

The sensors fused may be of the same or different types. Key features of data fusion in sensor networks, that distinguish it from other methods to combine...

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Recommended Reading

  1. Berger T, Zhang Z, Vishwanathan H. The CEO problem. IEEE Trans Inf Theory. 1996;42(3):887–902.

    Article  MathSciNet  MATH  Google Scholar 

  2. Brooks RR, Iyengar SS. Multi-sensor fusion: fundamentals and applications with software. Englewood: Prentice-Hall; 1997.

    Google Scholar 

  3. Crowley JL, Demazeau Y. Principles and techniques for sensor data fusion. Signal Process. 1993;32(1–2):5–27.

    Article  Google Scholar 

  4. Funiak S, Guestrin C, Paskin M, Sukthankar R. Distributed inference in dynamical systems. In: Scholkopf B, Platt J, Hoffman T, editors. Advances in neural information processing systems 19. Cambridge, MA: MIT; 2006. p. 433–40.

    Google Scholar 

  5. Hall DL, McMullen SAH. Mathematical techniques in multisensor data fusion. Boston: Artech House; 2004.

    MATH  Google Scholar 

  6. Isard M, Blake A. Condensation - conditional density propagation for visual tracking. Int J Comput Vis. 1998;29(1):5–28.

    Article  Google Scholar 

  7. Jazwinsky A. Stochastic processes and filtering theory. New York: Academic; 1970.

    Google Scholar 

  8. Lodaya MD, Bottone R. Moving target tracking using multiple sensors. In: Proceedings of the SPIE – The International Society for Optical Engineering; 200. p. 333–44.

    Google Scholar 

  9. Maybeck PS. The kalman filter: an introduction to concepts. In: Cox IJ, Wilfong GT, editors. Autonomous robot vehicles. New York: Springer; 1990. p. 194–204.

    Chapter  Google Scholar 

  10. Olfati-Saber R. Distributed kalman filtering for sensor networks. In: Proceedings of the 46th IEEE Conference on Decision and Control; 2007.

    Google Scholar 

  11. Olfati-Saber R, Shamma JS. Consensus filters for sensor networks and distributed sensor fusion. In: Proceedings of the 44th IEEE Conference on Decision and Control; 2005.

    Google Scholar 

  12. Oohama Y. The rate distortion function for the quadratic Gaussian CEO problem. IEEE Trans Inf Theory. 1998;44(3)

    Article  MathSciNet  MATH  Google Scholar 

  13. Pandya A, Kansal A, Pottie GJ, Srivastava MB. Fidelity and resource sensitive data gathering. In: Proceedings of the 42nd Annual Allerton Conference on Communication, Control, and Computing; 2004.

    Google Scholar 

  14. Paskin M, Guestrin C, McFadden J. A robust architecture for distributed inference in sensor networks. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks; 2005.

    Google Scholar 

  15. Rao B, Durrant-Whyte H, Sheen J. A fully decentralized multi-sensor system for tracking and surveillance. Int J Robot Res. 1993;12(1):20–44.

    Article  Google Scholar 

  16. Speyer JL. Computation and transmission requirements for a decentralized linear-quadratic-Gaussian control problem. IEEE Trans Autom Control. 1979;24(2):266–9.

    Article  MATH  Google Scholar 

  17. Zhao F, Liu J, Liu J, Guibas L, Reich J. Collaborative signal and information processing: an information directed approach. Proc IEEE. 2003;91(8):1199–209.

    Article  Google Scholar 

  18. Zhao F, Guibas L. Wireless sensor networks: an information processing approach. San Francisco: Morgan Kaufmann; 2004.

    Google Scholar 

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Correspondence to Aman Kansal .

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Kansal, A., Zhao, F. (2018). Data Fusion in Sensor Networks. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_101

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