Energy efficient sampling for event detection in wireless sensor networks

Proceedings of the International Symposium on Low Power Electronics and Design

Volumn , Issue , 2009, Pages 419-424

  Charbiwala, Zainul ^a   Kim, Younghun ^a   Zahedi, Sadaf ^a   Friedman, Jonathan ^a   Srivastava, Mani B ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Compressive sensing; Detection; Wireless sensor networks

Indexed keywords

COMPRESSIVE SENSING; ENERGY EFFICIENT; ENERGY-CONSTRAINED; EVENT DETECTION; PRACTICAL IMPLEMENTATION; SAMPLING PROCESS; SAMPLING RATES; SIGNAL ACQUISITIONS; WIRELESS SENSOR NODE;

ELECTRIC NETWORK SYNTHESIS; ENERGY EFFICIENCY; LOW POWER ELECTRONICS; POWER ELECTRONICS; SENSOR NETWORKS; SENSOR NODES; SIGNAL DETECTION; SIGNAL PROCESSING; SIGNAL RECONSTRUCTION; TELECOMMUNICATION EQUIPMENT; WIRELESS TELECOMMUNICATION SYSTEMS;

WIRELESS SENSOR NETWORKS;

EID: 70449711347     PISSN: 15334678     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1594233.1594339     Document Type: Conference Paper

References (21)

1. ALLEN, M., GIROD, L., NEWTON, R., MADDEN, S., BLUMSTEIN, D. T., AND ESTRIN, D. Voxnet: An interactive, rapidly-deployable acoustic monitoring platform. IPSN08.
2. BARANIUK, R., DAVENPORT, M., DEVORE, R., AND WAKIN, M. A simple proof of the restricted isometry property for random matrices. Constructive Approximation (2008).
3. Bilinskis, I., and Mikelson, A. Randomized Signal Processing. Prentice-Hall, Inc. NJ, USA, 1992.
4. Boyle, F., Haupt, J., Fudge, G., and Yeh, C. Detecting Signal Structure from Randomly-Sampled Data. In Statistical Signal Processing (2007).
5. CANDES, E., ROMBERG, J., AND TAO, T. ROBUST uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52, 2 (2006), 489-509.
6. CANDES, E., AND WAKIN, M. People hearing without listening: An introduction to compressive sampling. IEEE Signal Processing Magazine.
7. CANDES, E., WAKIN, M., AND BOYD, S. Enhancing sparsity by reweighted l1 minimization. Journal of Fourier Analysis and Applications 14, 5-6 (2008), 877-905.
8. 1 minimization for event detection in sensor networks. NESL Tech Report, http://nesl.ee.ucla.edu/document/show/299 (2009).
9. CHEN, S., DONOHO, D., AND SAUNDERS, M. Atomic Decomposition by Basis Pursuit. SIAM Journal on Scientic Computing 20, 1 (1998), 33-61.
10. DANG, T., BULUSU, N., AND HU, W. Lightweight Acoustic Classication for Cane-Toad Monitoring. Asilomar Conference on Signals, Systems and Computers (2008).
11. DAVENPORT, M., DUARTE, M., WAKIN, M., LASKA, J., TAKHAR, D., KELLY, K., AND BARANIUK, R. The smashed lter for compressive classication and target recognition. In SPIE (2007).
12. DUARTE, M., DAVENPORT, M., WAKIN, M., AND BARANIUK, R. Sparse Signal Detection from Incoherent Projections. In ICASSP (2006).
13. GRIFFIN, A., AND TSAKALIDES, P. Compressed Sensing of Audio Signals Using Multiple Sensors. EUSIPCO08.
14. HAUPT, J., AND NOWAK, R. Compressive Sampling for Signal Detection. In ICASSP (2007).
15. 1 minimization for sparse recovery with prior information.
16. KIROLOS, S., LASKA, J., WAKIN, M., DUARTE, M., BARON, D., RAGHEB, T., MASSOUD, Y., AND BARANIUK, R. Analog-to-information conversion via random demodulation. In DCAS (2006).
17. L'ECUYER, P. Efficient and portable combined random number generators.
18. MCDONOUGH, R. N., AND WHALEN, A. Detection of Signals in Noise, 2nd edition, Academic Press. 1995.
19. RUDELSON, M., AND VERSHYNIN, R. Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements. In Information Sciences and Systems, (2006).
20. TITZER, B., LEE, D., AND PALSBERG, J. Avrora: Scalable sensor network simulation with precise timing. In IPSN'05.
21. TROPP, J., AND GILBERT, A. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit. Information Theory, IEEE Transactions on (2007).