Vol. 122
Latest Volume
All Volumes
PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
2011-12-05
Optimized Local Superposition in Wireless Sensor Networks with T-Average-Mutual-Coherence
By
Progress In Electromagnetics Research, Vol. 122, 389-411, 2012
Abstract
Compressed sensing (CS) is a new technology for recovering sparse data from undersampled measurements. It shows great potential to reduce energy for sensor networks. First, a basic global superposition model is proposed to obtain the measurements of sensor data, where a sampling matrix is modeled as the channel impulse response (CIR) matrix while the sparsifying matrix is expressed as the distributed wavelet transform (DWT). However, both the sampling and sparsifying matrixes depend on the location of sensors, so this model is highly coherent. This violates the assumption of CS and easily produces high data recovery error. In this paper, in order to reduce the coherence, we propose to control the transmit power of some nodes with the help of t-average-mutual-coherence, and recovery quality are greatly improved. Finally, to make the approach more realistic and energy-efficient, the CIR superposition is restricted in local clusters. Two key parameters, the radius of power control region and the radius of local clusters, are optimized based on the coherence and resource consideration in sensor networks. Simulation results demonstrate that the proposed scheme provides a high recovery quality for networked data and verify that t-average-mutual-coherence is a good criterion for optimizing the performance of CS in our scenario.
Citation
Di Guo, Xiaobo Qu, Lianfen Huang, and Yan Yao, "Optimized Local Superposition in Wireless Sensor Networks with T-Average-Mutual-Coherence," Progress In Electromagnetics Research, Vol. 122, 389-411, 2012.
doi:10.2528/PIER11072605
References

1. Heinzelman, W. B., A. P. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Trans. Wireless Commun., Vol. 1, No. 4, 660-670, 2002.
doi:10.1109/TWC.2002.804190

2. Donoho, D. L., "Compressed sensing," IEEE Trans. Inf. Theory, Vol. 52, No. 4, 1289-1306, 2006.
doi:10.1109/TIT.2006.871582

3. Candes, E. J. and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Process. Mag., Vol. 25, No. 2, 21-30, 2008.
doi:10.1109/MSP.2007.914731

4. Haupt, J., W. U. Bajwa, M. Rabbat, and R. Nowak, "Compressed sensing for networked data," IEEE Signal Process. Mag., Vol. 25, No. 2, 92-101, 2008.
doi:10.1109/MSP.2007.914732

5. Duarte, M. F., S. Sarvotham, D. Baron, M. B. Wakin, and R. G. Baraniuk, "Distributed compressed sensing of jointly sparse signals," 39th Asilomar Conf. Signals, Systems and Computers, 1537-1541, 2005.

6. Bajwa, W., J. Haupt, A. Sayeed, and R. Nowak, "Compressive wireless sensing," 5th Int. Conf. Information Processing in Sensor Networks, 134-142, 2006.

7. Lee, S., S. Pattem, M. Sathiamoorthy, B. Krishnamachari, and A. Ortega, "Spatially-localized compressed sensing and routing in multi-hop sensor networks ," 3rd Int. Conf. on Geo. Sensor Networks, 11-20, 2009.

8. Jia, M., L. Husheng, and H. Zhu, "Sparse event detection in wireless sensor networks using compressive sensing," 43rd Annu. Conf. Information Sciences and Systems, 181-185, 2009.

9. Wei, S.-J., X.-L. Zhang, J. Shi, and G. Xiang, "Sparse reconstruction for SAR imaging based on compressed sensing," Progress In Electromagnetics Research, Vol. 109, 63-81, 2010.
doi:10.2528/PIER10080805

10. Wei, S.-J., X.-L. Zhang, and J. Shi, "Linear array SAR imaging via compressed sensing," Progress In Electromagnetics Research, Vol. 117, 299-319, 2011.

11. Zhang, Y., L.Wu, B. Peterson, and Z. Dong, "A two-level iterative reconstruction method for compressed sensing MRI," Journal of Electromagnetic Waves and Applications, Vol. 25, No. 8-9, 1081-1091, 2011.
doi:10.1163/156939311795762024

12. Elad, M., Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer Verlag, 2010.

13. Elad, M., "Optimized projections for compressed sensing," IEEE Trans. Signal Process., Vol. 55, No. 2, 5695-5702, 2007.
doi:10.1109/TSP.2007.900760

14. Gastpar, M. and M. Vetterli, "Power, spatio-temporal bandwidth, and distortion in large sensor networks," IEEE J. Sel. Areas Commun., Vol. 23, No. 4, 745-754, 2005.
doi:10.1109/JSAC.2005.843542

15. Guo, D., X. Qu, L. Huang, and Y. Yao, "Sparsity-based spatial interpolation in wireless sensor networks," Sensors, Vol. 11, No. 3, 2385-2407, 2011.
doi:10.3390/s110302385

16. Quer, G., R. Masiero, D. Munaretto, M. Rossi, J. Widmer, and M. Zorzi, "On the interplay between routing and signal representation for compressive sensing in wireless sensor networks," Information Theory and Applications Workshop, 206-215, 2009.
doi:10.1109/ITA.2009.5044947

17. Andersen, J. B., T. S. Rappaport, and S. Yoshida, "Propagation measurements and models for wireless communications channels," IEEE Commun. Mag., Vol. 33, No. 1, 42-49, 1995.
doi:10.1109/35.339880

18. Gay-Fernandez, J. A., M. Garcia Sanchez, I. Cui~nas, A. V. Alejos, J. G. Sanchez, and J. L. Miranda-Sierr, "Propagation analysis and deployment of a wireless sensor network in a forest," Progress In Electromagnetics Research, Vol. 106, 121-145, 2010.
doi:10.2528/PIER10040806

19. Patwari, N. and S. K. Kasera, "Robust location distinction using temporal link signatures," 13th ACM Int. Conf. on Mobile Computing and Networking, 111-122, 2007.

20. Alsehaili, M., S. Noghanian, A. R. Sebak, and D. A. Buchanan, "Angle and time of arrival statistics of a three dimensional geometrical scattering channel model for indoor and outdoor propagation environments," Progress In Electromagnetics Research, Vol. 109, 191-209, 2010.
doi:10.2528/PIER10081106

21. Chen, B., R. Jiang, T. Kasetkasem, and P. K. Varshney, "Channel aware decision fusion in wireless sensor networks," IEEE Trans. Signal Process., Vol. 52, No. 12, 3454-3458, 2004.
doi:10.1109/TSP.2004.837404

22. Wagner, R. S., R. G. Baraniuk, S. Du, D. B. Johnson, and A. Cohen, "An architecture for distributed wavelet analysis and processing in sensor networks," 5th Int. Conf. Information Processing in Sensor Networks, 243-250, 2006.

23. Donoho, D., V. Stodden, and Y. Tsaig, Sparselab [Online]. Available: http://sparselab.stanford.edu/.

24. Mitilineos, S. A. and S. C. A. Thomopoulos, "Positioning accuracy enhancement using error modeling via a polynomial approximation approach ," Progress In Electromagnetics Research, Vol. 102, 49-64, 2010.
doi:10.2528/PIER10010102

25. Reza, A. W., S. M. Pillai, K. Dimyati, and K. G. Tan, "A novel positioning system utilizing zigzag mobility pattern," Progress In Electromagnetics Research, Vol. 106, 263-278, 2010.
doi:10.2528/PIER10060904

26. Mitilineos, S. A., D. M. Kyriazanos, O. E. Segou, J. N. Goufas, and S. C. A. Thomopoulos, "Indoor localisation with wireless sensor networks," Progress In Electromagnetics Research, Vol. 109, 441-474, 2010.
doi:10.2528/PIER10062801