Sparse event detection in wireless sensor networks using compressive sensing

Proceedings - 43rd Annual Conference on Information Sciences and Systems, CISS 2009

Volumn , Issue , 2009, Pages 181-185

  Meng, Jia ^a   Li, Husheng ^b   Han, Zhu ^a  

^a UNIVERSITY OF HOUSTON   (United States)

^b University of Tennessee Knoxville   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN DETECTION; DEPLOYMENT COSTS; DETECTION ALGORITHM; ENERGY CONSTRAINT; EVENT DETECTION; GAUSSIAN NOISE; PRIOR INFORMATION; SAMPLING RATES; SENSING PROBLEMS; SIMULATION RESULT; SPARSE SIGNALS;

GAUSSIAN NOISE (ELECTRONIC); INFORMATION SCIENCE; SENSOR NETWORKS; SIGNAL RECONSTRUCTION;

WIRELESS SENSOR NETWORKS;

EID: 70349659937     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISS.2009.5054713     Document Type: Conference Paper

References (18)

1. E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information", IEEE Trans. on Information Theory, 52(2) pp. 489-509, February 2006.
2. E. Candes and T. Tao, "Near optimal signal recovery from random projections: Universal encoding strategies?", IEEE Trans. on Information Theory, 52(12), pp. 5406-5425, December 2006.
3. D. Donoho, "Compressed sensing", IEEE Trans. on Information Theory, 52(4), pp. 1289-1306, April 2006.
4. W. U. Bajwa, J. Haupt, G. Raz, and R. Nowak, "Compressed channel sensing", Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, March 2008.
5. G. Taubock and F. Hlawatsch, "A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots", IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008.
6. Y. Mostofi and P. Sen, "Compressed mapping of communication signal strength", Military Communications Conference, San Diego, CA, November 2008.
7. Z. Tian, "Compressed wideband sensing in cooperative cognitive radio networks," Proc. IEEE Globecom Conf, pp. 1-5, New Orleans, Dec. 2008.
8. B. Krishnamachari, Networking wireless sensors, Cambridge University Press, UK, 2005.
9. X. Li, Wireless ad hoc and sensor networks, Cambridge University Press, UK, 2008.
10. S. Boyd and L. Vandenberghe, Convex optimization, Cambridge University Press, 2006. (http://www.stanford.edu/~boyd/cvxbook.html)
11. Z. Han and K. J. R. Liu. Resource allocation for wireless networks: basics, techniques, and applications. Cambridge University Press, Cambridge, UK, 2008.
12. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine", Journal of Machine Learning Research, vol. 1, p.p. 211-244, Sept. 2001.
13. M. E. Tipping and A. C. Faul, "Fast marginal likelihood maximisation for sparse Bayesian models", in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6.
14. S. Ji, Y. Xue and L. Carin, "Bayesian compressive sensing", IEEE Trans. Signal Processing, vol. 56, no. 6, June 2008.
15. Rice University, LI-Related Optimization Project, available from http://www.caam.rice.edu/6ptimization/Ll/
16. R. G. Baraniuk, "Compressive sensing", lecture notes, Rice university.
17. Compressive sensing resource, http://www.dsp.ece.rice.edu/cs/
18. D. J. C. MacKay, "A practical Bayesian framework for backprop networks", Neural Computation, 4:448-472, 1992.