How much information can one get from a wireless ad hoc sensor network over a correlated random field?

IEEE Transactions on Information Theory

Volumn 55, Issue 6, 2009, Pages 2827-2847

  Sung, Youngchul ^a   Poor, H Vincent ^b   Yu, Heejung ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Princeton University   (United States)

Author keywords

Ad hoc sensor networks; Asymptotic Kullback Leibler information rate; Asymptotic mutual information rate; Conditional autoregressive model; Gauss Markov random fields; Large deviations principle; Stationary Gaussian fields

Indexed keywords

AD HOC SENSOR NETWORKS; ASYMPTOTIC KULLBACK-LEIBLER INFORMATION RATE; ASYMPTOTIC MUTUAL INFORMATION RATE; CONDITIONAL AUTOREGRESSIVE MODEL; GAUSS-MARKOV RANDOM FIELDS; LARGE DEVIATIONS PRINCIPLE; STATIONARY GAUSSIAN FIELDS;

AD HOC NETWORKS; ASYMPTOTIC ANALYSIS; COMPUTATIONAL FLUID DYNAMICS; ENERGY EFFICIENCY; GAUSSIAN DISTRIBUTION; HIDDEN MARKOV MODELS; IMAGE RESOLUTION; IMAGE SEGMENTATION; REGRESSION ANALYSIS; SENSOR NODES; TELECOMMUNICATION EQUIPMENT; WIRELESS SENSOR NETWORKS;

SENSOR NETWORKS;

EID: 66949160796     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIT.2009.2018333     Document Type: Article

References (30)

1. D. Estrin, D. Culler,K. Pister, and G. Sukhatme, "Connecting the physical world with pervasive networks," IEEE Pervasive Computing vol. 1, pp. 59-69, Jan.-Mar. 2002.
2. J.-F. Chamberland and V. V. Veeravalli, "How dense should a sensor network be for detection with correlated observations?," IEEE Trans. Inf. Theory, vol. 52, no. 11, pp. 5099-5106, Nov. 2006.
3. M. Dong, L. Tong, and B. M. Sadler, "Impact of data retrieval pattern on homogeneous signal field reconstruction in dense sensor networks," IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4352-4364, Nov. 2006.
4. Y. Sung, X. Zhang, L. Tong, and H. V. Poor, "Sensor configuration and activation for field detection in large sensor arrays," IEEE Trans. Signal Process., vol. 56, no. 2, pp. 1354-1365, Feb. 2008.
5. L. S. Pillutla and V. Krishnamurthy, "Mutual information and energy tradeoff in correlated wireless sensor networks," in Proc. 2008 IEEE Int. Conf. Communications (ICC), Beijing, China, May 2008, pp. 4402-4406.
6. F. Liese and I. Vajda, "On divergence and informations in statistics and information theory," IEEE Trans. Inf. Theory, vol. 52, no. 10, pp. 4394-4412, Oct. 2006.
7. S. Kullback, Information Theory and Statistics. Mineola, NY: Dover, 1997.
8. M. D. Donsker and S. R. S. Varadhan, "Large deviations for stationary Gaussian process," Commun. Math. Phys., vol. 97, pp. 187-210, 1985.
9. G. R. Benitz and J. A. Bucklew, "Large deviation rate calculations for nonlinear detectors in Gaussian noise," IEEE Trans. Inf. Theory, vol. 36, no. 2, pp. 358-371, Mar. 1990.
10. W. Bryc and A. Dembo, "Large deviations for quadratic functionals of Gaussian processes," J. Theor. Probab., vol. 10, no. 2, pp. 307-332, 1997.
11. B. Bercu, F. Gamboa, and A. Rouault, "Large deviations for quadratic forms of stationary Gaussian processes," Stochastic Processes and Their Applications, vol. 71, pp. 75-90, 1997.
12. Y. Sung, L. Tong, and H. V. Poor, "Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1354-1365, Apr. 2006.
13. A. S. Leong, S. Dey, and J. S. Evans, "Error exponents for Neyman-Pearson detection of Markov chains in noise," IEEE Trans. Signal Process., vol. 55, no. 10, pp. 5097-5103, Oct. 2007.
14. A. Anandkumar, L. Tong, and A. Swami, "Detection of Gauss-Markov random field on nearest-neighbor graph," in Proc. 2007 IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP'07), Honolulu, HI, Apr. 2007, pp. III-829-III-832.
15. Y. Sung, H. V. Poor, and H. Yu, "Information, energy and density for ad hoc sensor networks over correlated random fields: Large deviations analysis," in Proc. 2008 Int. Symp. Information Theory (ISIT), Toronto, ON, Canada, Jul. 2008, pp. 1592-1596.
16. H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applicatons. New York: Chapman & Hall/CRC, 2005.
17. R. R. Bahadur, S. L. Zabell, and J. C. Gupta, "Large deviations, tests, and estimates," in Asymptotic Theory of Statistical Tests and Estimation: In Honor of Wassily Hoeffding, I. M. Chakravarti, Ed. New York: Academic, 1980.
18. J. T. Kent and K. V. Mardia, "Spectral and circulant approximations to the likelihood for stationary Gaussian random fields," J. Statist. Planning and Inference, vol. 502, no. 3, pp. 379-394, 1996.
19. R. R. Bahadur, "Some limit theorems in statistics," in Proc. Conf. Board of the Mathematical Sciences Regional Conferences (Series in Applied Mathematics), Philadelphia, PA, 1971.
20. T. Cover and J. Thomas, Elements of Information Theory. New York: Wiley, 1991.
21. J. Besag, "On a system of two-dimensional recurrence equations," J. Roy. Statist. Soc. Ser. B, vol. 43, no. 3, pp. 302-309, 1981.
22. A. Erdélyi, Higher Transcendental Functions, Vol. II. NewYork: McGraw-Hill, 1953.
23. P. Whittle, "On stationary processes in the plane," Biometrika, vol. 41, pp. 434-449, Dec. 1954.
24. I. Vajda, Theory of Statistical Inference and Information. Dordrecht, The Netherlands: Kluwer Academic, 1989.
25. P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, 2nd ed. New York: Springer, 1991.
26. E. J. Hannon, "The asymptotic theory of linear time-series models," J. Appl. Probab., vol. 10, pp. 130-145, Mar. 1973.
27. X. Guyon, Random Fields on a Network: Modeling, Statistics and Applications. New York: Springer-Verlag, 1995.
28. I. Gohberg, S. Goldberg, and M. A. Kaashoek, Basic Classes of Linear Operators. Basel, Switzerland: Birkhäuser, 2004.
29. R. J. Martin, "A note on the asymptotic eigenvalues and eigenvectors of the dispersion matrix of a second-order stationary process on a d dimensional lattice," J. Appl. Probab., vol. 23, pp. 529-535, Jun. 1986.
30. P. J. Davis, Circulant Matrices. New York, NY: Chelsea, 1994.