Energy and Distortion Minimization in “Refining” and “Expanding” Sensor Networks

  • Franco Davoli
  • Mario Marchese
  • Maurizio Mongelli
Conference paper


We consider two instances of different models of sensor networks, introduced in (IEEE Signal Process Mag 23(4):70-83, 2006) and termed “refining” and “expanding,” respectively. The first one refers to the acquisition of measurements from a source, representing a physical phenomenon, by means of sensors deployed at different distances, and measuring random variables that are correlated with the source output. The acquired values are transmitted to a sink, where an estimation of the source has to be constructed, according to a given distortion criterion. The second model represents a “rich” communication infrastructure, where all sensor readings potentially bring fresh information to the sink. In both cases, we want to investigate coding strategies that obey a global power constraint and are decentralized (each sensor’s decision is based solely on the variable it observes). The sensors and the sink act as the members of a team, i.e., they possess different information and they share a common goal, which consists in minimizing the expected distortion on the variables of interest. In the presence of Gaussian sources and a Gaussian vector channel, with quadratic distortion function, the coding/decoding strategies are sought by means of approximating functions based on back-propagation neural networks. We compare the nonlinear team solutions with the best linear ones.


Sensor Node Wireless Sensor Network Power Allocation Voronoi Diagram Power Constraint 
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  1. 1.
    Gastpar M, Vetterli M, Dragotti PL (2006) Sensing reality and communicating bits: a dangerous liaison. IEEE Signal Process Mag 23(4):70–83CrossRefGoogle Scholar
  2. 2.
    Gastpar M (2008) Uncoded transmission is exactly optimal for a simple Gaussian ‘sensor’ network. IEEE Trans Inf Theory 54(11):5247–5251CrossRefMathSciNetGoogle Scholar
  3. 3.
    Wei S, Kannan R, Iyengar SS, Rao NS (2008) Energy efficient estimation of Gaussian sources over inhomogenous Gaussian MAC channels. Proceedings of IEEE Globecom 2008, New Orleans, LA, Nov–Dec 2008, pp 1–5Google Scholar
  4. 4.
    Vuran MC, Akan ÖB, Akyildiz IF (2004) Spatio-temporal correlation: theory and applications for wireless sensor networks. Comput Netw 45:245–259MATHCrossRefGoogle Scholar
  5. 5.
    Davoli F, Marchese M, Mongelli M (2009) A decision theoretic approach to Gaussian sensor networks. Proceedings of adhoc and sensor networking symposium, IEEE international conference on commununications 2009 (ICC 2009), Dresden, Germany, June 2009Google Scholar
  6. 6.
    Ho YC, Kastner MP, Wong E (1987) Teams, signaling, and information theory. IEEE Trans Automat Contr AC-23:305–311Google Scholar
  7. 7.
    Zoppoli R, Sanguineti M, Parisini T (2002) Approximating networks and extended Ritz method for the solution of functional optimization problems. J Optim Theory Appl 112(2):403–439MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Witsenhausen HS (1968) A counterexample in stochastic optimum control. SIAM J Control 6:131–147MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Pilc RJ (1969) The optimum linear modulator for a Gaussian source used with a Gaussian channel. Bell Syst Tech J 48:3075–3089MathSciNetGoogle Scholar
  10. 10.
    Vuran MC, Akyildiz IF (2006) Spatial correlation-based collaborative medium access control in wireless sensor networks. IEEE/ACM Trans Networking 14 (2):316–329CrossRefGoogle Scholar
  11. 11.
    Kushner HJ, Yin GG (1997) Stochastic approximation algorithms and applications. Springer-Verlag, New York, NYMATHGoogle Scholar
  12. 12.
    Portela JN, Alencar MS (2005) Spatial analysis of the overlapping cell area using Voronoi diagrams. Proceedings of IEEE microwave and optoelectronics, Brasilia, Brazil, 25–28 July 2005, pp 643–646Google Scholar
  13. 13.
    Ho Y-C, Chu K-C (1972) Team decision theory and information structures in optimal control problems – Part I. IEEE Trans Automat Contr AC-17(1):15–22Google Scholar
  14. 14.
    Marschak J, Radner R (1972) Economic theory of teams. Yale University Press, New Haven, CTMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Franco Davoli
    • 1
  • Mario Marchese
    • 1
  • Maurizio Mongelli
    • 1
  1. 1.DIST-University of GenoaGenoaItaly

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