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Distributed Detection of Spatially Non-constant Phenomena

  • Chiara BurattiEmail author
  • Marco Martalò
  • Roberto Verdone
  • Gianluigi Ferrari
Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

In this chapter, we study sensor networks with distributed detection of a spatially non-constant phenomenon. In particular, we consider binary phenomena characterized by a generic number of status changes (from state “0” to state “1” or vice-versa) across the sensors. We first derive the Mean Square Error (MMSE) fusion algorithm at the Access Point (AP). Then, we propose simplified (sub-optimum) fusion algorithms at the AP, with a lower computational complexity. While we first consider a scenario with ideal communication links between the sensors and the AP, we then extend our framework to scenarios with noisy communication links.

References

  1. 1.
    A. Abrardo, G. Ferrari, M. Martalò, Non-cooperative wireless orthogonal multiple access schemes with and without relaying, in IEEE International Symposium on Communications, Control and Signal Processing (ISCCSP), St. Julians, Malta, March 2008, pp. 455–460Google Scholar
  2. 2.
    G. Ferrari, M. Martalò, M. Sarti, Reduced-complexity decentralized detection of spatially non-constant phenomena, in Proceedings of International Workshop on Distributed Cooperative Laboratories (Ingrid), Santa Margherita Ligure, Italy, April 2007Google Scholar
  3. 3.
    S.M. Kay, Fundamentals of Statistical Signal Processing, vol. 1, Estimation Theory (Prentice-Hall, Upper Saddle River, 1993)Google Scholar
  4. 4.
    A. Papoulis, Probability, Random Variables and Stochastic Processes (McGraw-Hill, New York, 1991)Google Scholar
  5. 5.
    F.R. Kschischang, B.J. Frey, H.A. Loeliger, Factor graphs and the sum–product algorithm. IEEE Trans. Inform. Theory 47(2), 498–519 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    R. Nowak, U. Mitra, Boundary estimation in sensor networks: theory and methods, in Proceedings of International Work. on Information Processing in Sensor Networks (IPSN), Palo Alto, CA, USA, April 2003, pp. 80–95Google Scholar
  7. 7.
    J.G. Proakis, Digital Communications, 4th edn. (McGraw-Hill, New York, 2001)Google Scholar
  8. 8.
    T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms, 2nd edn. (MIT Press, Cambridge, 2002)Google Scholar
  9. 9.
    R. Nowak, U. Mitra, R. Willett, Estimating inhomogeneous fields using wireless sensor networks. IEEE J. Sel. Areas Commun. 22(6), 999–1006 (2004)CrossRefGoogle Scholar
  10. 10.
    J. Barros, M. Tückler, Scalable decoding on factor trees: a practical solution for wireless sensor networks. IEEE Trans. Commun. 54(2), 284–294 (2006)CrossRefGoogle Scholar
  11. 11.
    J.-J. Luo, Z.-Q. Luo, Universal decentralized detection in a bandwidth constrained sensor network. IEEE Trans. Signal Process. 53(8), 2617–2624 (2005)CrossRefMathSciNetGoogle Scholar
  12. 12.
    S. Duttagupta, K. Ramamritham, Distributed boundary estimation using sensor networks (Indian Institute of Technology, Mumbai, Tech. Rep., 2006), available at http://www.it.iitb.ac.in/research/techreport/
  13. 13.
    S. Duttagupta, K. Ramamritham, P. Ramanathan, Distributed boundary estimation using sensor networks, in Proceedings of International Conference on Mobile Ad-hoc and Sensor Systems (MASS), Vancouver, Canada, October 2006, pp. 316–325Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chiara Buratti
    • 1
    Email author
  • Marco Martalò
    • 2
  • Roberto Verdone
    • 1
  • Gianluigi Ferrari
    • 2
  1. 1.Dipto. Elettronica, Informatica e Sistemistica (DEIS) Università di BolognaBolognaItaly
  2. 2.Dipto. Ingegneria dell’InformazioneUniversità di ParmaParmaItaly

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