Sequential Compressed Sensing with Progressive Signal Reconstruction in Wireless Sensor Networks

IEEE Transactions on Wireless Communications

Volumn 14, Issue 3, 2015, Pages 1622-1635

  Leinonen, Markus ^a   Codreanu, Marian ^a   Juntti, Markku ^a  

^a UNIVERSITY OF OULU   (Finland)

Author keywords

Compressed sensing (CS); environmental sensing; Kronecker sparsifying basis; recursive signal recovery; regularization; reweighted 1 minimization; sliding window; spatio temporal correlation; streaming data; wireless sensor networks (WSNs)

Indexed keywords

COMPLEX NETWORKS; COMPRESSED SENSING; COMPUTER SYSTEM RECOVERY; DATA COMMUNICATION SYSTEMS; DECODING; ECONOMIC AND SOCIAL EFFECTS; NUMERICAL METHODS; RECOVERY; SIGNAL RECONSTRUCTION;

COMPRESSIVE SENSING; ENVIRONMENTAL SENSING; KRONECKER; REGULARIZATION; SIGNAL RECOVERY; SLIDING WINDOW; SPATIOTEMPORAL CORRELATION; STREAMING DATA; WIRELESS SENSOR NETWORK (WSNS);

WIRELESS SENSOR NETWORKS;

EID: 84924910090     PISSN: 15361276     EISSN: None     Source Type: Journal    
DOI: 10.1109/TWC.2014.2371017     Document Type: Article

References (89)

1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: A survey," Comput. Netw., vol. 38, no. 4, pp. 393-422, Mar. 2002.
2. D. Estrin, D. Culler, K. Pister, and G. Sukhatme, "Connecting the physical world with pervasive networks," IEEE Pervasive Comput., vol. 1, no. 1, pp. 59-69, Jan.-Mar. 2002.
3. Z. Xiong, A. Liveris, and S. Cheng, "Distributed source coding for sensor networks," IEEE Signal Process. Mag., vol. 21, no. 5, pp. 80-94, Jul. 2004.
4. J.-J. Xiao, A. Ribeiro, Z.-Q. Luo, and G. Giannakis, "Distributed compression-estimation using wireless sensor networks," IEEE Signal Process. Mag., vol. 23, no. 4, pp. 27-41, Jul. 2006.
5. I. F. Akyildiz, M. C. Vuran, and B. Akan, "On exploiting spatial and temporal correlation in wireless sensor networks," in Proc. Model. Optim. Mobile, Ad Hoc WiOpt, Cambridge, U.K., Mar. 24-26, 2004, pp. 71-80.
6. E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,"IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
7. D. L. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
8. E. Candes, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Commun. Pure Appl. Math., vol. 59, no. 8, pp. 1207-1223, Aug. 2006.
9. J. Haupt and R. Nowak, "Signal reconstruction from noisy random projections," IEEE Trans. Inf. Theory, vol. 52, no. 9, pp. 4036-4048, Sep. 2006.
10. E. Candes and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?" IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
11. E. Candes, "Compressive sampling," in Proc. Int. Congr. Math., Madrid, Spain, Aug. 22-30, 2006, vol. 3, pp. 1433-1452.
12. R. Baraniuk, "Compressive sensing [lecture notes]," IEEE Signal Process. Mag., vol. 24, no. 4, pp. 118-121, Jul. 2007.
13. E. Candes and M. Wakin, "An introduction to compressive sampling,"IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.
14. M. Mishali and Y. Eldar, "Xampling: Compressed sensing of analog signals,"in Compressive Sensing: Theory and Applications, Y. C. Eldar and G. Kutyniok, Eds. Cambridge, U.K.: Cambridge Univ. Press, Nov. 2012, ch. 3, pp. 88-147.
15. th Int. Conf. Inf. Process. Sens. Netw., New York, NY, USA, 2006, pp. 177-185.
16. J. Haupt, W. Bajwa, M. Rabbat, and R. Nowak, "Compressed sensing for networked data: A different approach to decentralized compression,"IEEE Signal Process. Mag., vol. 25, no. 2, pp. 92-101, Mar. 2008.
17. S. Li, L. Xu, and X. Wang, "Compressed sensing signal and data acquisition in wireless sensor networks and Internet of things," IEEE Trans. Ind. Informat., vol. 9, no. 4, pp. 2177-2186, Nov. 2013.
18. G. Quer et al., "On the interplay between routing and signal representation for compressive sensing in wireless sensor networks," in Proc. Inf. Theory Appl. Workshop, San Diego, CA, USA, Feb. 8-13, 2009, pp. 206-215.
19. th Annu. Int. Conf. Mobile Comput. Netw., Beijing, China, Sep. 20-25, 2009, pp. 145-156.
20. C. Luo, F. Wu, J. Sun, and C. Chen, "Efficient measurement generation and pervasive sparsity for compressive data gathering," IEEE Trans. Wireless Commun., vol. 9, no. 12, pp. 3728-3738, Dec. 2010.
21. th Annu. IEEE Commun. Soc. Conf. Sens., Mesh Ad Hoc Commun. Netw., Salt Lake City, UT, USA, Jun. 27-30, 2011, pp. 46-54.
22. R. Masiero et al., "Data acquisition through joint compressive sensing and principal component analysis," in Proc. IEEE Global Telecommun. Conf., Honolulu, HI, USA, Nov. 30-Dec. 4, 2009, pp. 1-6.
23. G. Quer, R. Masiero, G. Pillonetto, M. Rossi, and M. Zorzi, "Sensing, compression, recovery for WSNs: Sparse signal modeling and monitoring framework," IEEE Trans. Wireless Commun., vol. 11, no. 10, pp. 3447-3461, Oct. 2012.
24. N. Nguyen, D. Jones, and S. Krishnamurthy, "Netcompress: Coupling network coding and compressed sensing for efficient data communication in wireless sensor networks," in Proc. IEEE Workshop SIPS, San Francisco, CA, USA, Oct. 6-8, 2010, pp. 356-361.
25. C. Luo, J. Sun, and F. Wu, "Compressive network coding for approximate sensor data gathering," in Proc. IEEE Global Telecommun. Conf., Houston, TX, USA, Dec. 5-9, 2011, pp. 1-6.
26. th Annu. Allerton Conf. Commun., Control, Comput., pp. 1048-1054.
27. J. Luo, L. Xiang, and C. Rosenberg, "Does compressed sensing improve the throughput of wireless sensor networks?" in Proc. IEEE Int. Conf. Commun., Cape Town, South Africa, May 23-27, 2010, pp. 1-6.
28. L. Xiang, J. Luo, and C. Rosenberg, "Compressed data aggregation: Energy-efficient and high-fidelity data collection," IEEE/ACM Trans. Netw., vol. 21, no. 6, pp. 1722-1735, Dec. 2013.
29. R. Xie and X. Jia, "Minimum transmission data gathering trees for compressive sensing in wireless sensor networks," in Proc. IEEE Global Telecommun. Conf., Houston, TX, USA, Dec. 5-9, 2011, pp. 1-5.
30. th Annu. IEEE Conf. Local Comput. Netw., Zurich, Switzerland, Oct. 20-23, 2009, pp. 443-450.
31. M. Sartipi and R. Fletcher, "Energy-efficient data acquisition in wireless sensor networks using compressed sensing," in Proc. DCC, Salt Lake County, UT, USA, Mar. 29-31, 2011, pp. 223-232.
32. D. Baron, M. Wakin, M. Duarte, S. Sarvotham, and R. Baraniuk, "Distributed compressed sensing," Rice University, Houston, UT, USA, Nov. 2005.
33. M. Duarte, M. Wakin, D. Baron, S. Sarvotham, and R. Baraniuk, "Measurement bounds for sparse signal ensembles via graphical models," IEEE Trans. Inf. Theory, vol. 59, no. 7, pp. 4280-4289, Jul. 2013.
34. M. Duarte and R. Baraniuk, "Kronecker compressive sensing," IEEE Trans. Image Process., vol. 21, no. 2, pp. 494-504, Feb. 2012.
35. Y. Rivenson and A. Stern, "Compressed imaging with a separable sensing operator," IEEE Signal Process. Lett., vol. 16, no. 6, pp. 449-452, Jun. 2009.
36. M. Duarte and R. Baraniuk, "Kronecker product matrices for compressive sensing," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Dallas, TX, USA, Mar. 14-19, 2010, pp. 3650-3653.
37. M. Leinonen, M. Codreanu, and M. Juntti, "Distributed correlated data gathering in wireless sensor networks via compressed sensing," in Proc. Annu. Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, USA, Nov. 3-6, 2013, pp. 418-422.
38. M. Leinonen, M. Codreanu, and M. Juntti, "Compressed acquisition and progressive reconstruction of multi-dimensional correlated data in wireless sensor networks," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Florence, Italy, May 4-9, 2014, pp. 6449-6453.
39. C. Caione, D. Brunelli, and L. Benini, "Compressive sensing optimization for signal ensembles in WSNs," IEEE Trans. Ind. Informat., vol. 10, no. 1, pp. 382-392, Feb. 2014.
40. 1-homotopy," IEEE Trans. Signal Process., vol. 62, no. 16, pp. 4209-4223, Aug. 2014.
41. N. Freris, O. Öc¸al, and M. Vetterli, "Recursive compressed sensing," IEEE Trans. Inf. Theory, 2013, submitted for publication. [Online]. Available: http://arxiv.org/abs/1312.4895v1
42. th IEEE Int. Conf. Image Process., San Diego, CA, USA, Oct. 12-15, 2008, pp. 893-896.
43. N. Vaswani, "LS-CS-residual (LS-CS): Compressive sensing on least squares residual," IEEE Trans. Signal Process., vol. 58, no. 8, pp. 4108-4120, Aug. 2010.
44. N. Vaswani and W. Lu, "Modified-CS: Modifying compressive sensing for problems with partially known support," IEEE Trans. Signal Process., vol. 58, no. 9, pp. 4595-4607, Sep. 2010.
45. W. Lu and N. Vaswani, "Regularized modified BPDN for noisy sparse reconstruction with partial erroneous support and signal value knowledge,"IEEE Trans. Signal Process., vol. 60, no. 1, pp. 182-196, Jan. 2012.
46. J. Zhan and N. Vaswani, "Time invariant error bounds for modified-CS based sparse signal sequence recovery," IEEE Trans. Inf. Theory, 2015, accepted for publication. [Online]. Available: http://arxiv.org/abs/1305.0842
47. N. Vaswani and W. Lu, "Recursive reconstruction of sparse signal sequences,"in Compressed Sensing & Sparse Filtering, A. Y. Carmi, L. Mihaylova, and S. J. Godsill, Eds. Berlin, Germany: Springer-Verlag, 2014, ser. Signals and Communication Technology, ch. 11, pp. 357-380.
48. D. Malioutov, S. Sanghavi, and A. Willsky, "Sequential compressed sensing,"IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 435-444, Apr. 2010.
49. J. Wang, S. Tang, B. Yin, and X.-Y. Li, "Data gathering in wireless sensor networks through intelligent compressive sensing," in Proc. IEEE INFOCOM, Mar. 2012, pp. 603-611.
50. G. Coluccia, S. Kuiteing, A. Abrardo, M. Barni, and E. Magli, "Progressive compressed sensing and reconstruction of multidimensional signals using hybrid transform/prediction sparsity model," IEEE J. Emerging Sel. Topics Circuits Syst., vol. 2, no. 3, pp. 340-352, Sep. 2012.
51. 1 minimization," J. Fourier Anal. Appl., vol. 14, no. 5, pp. 877-905, Dec. 2008.
52. D. Needell, "Noisy signal recovery via iterative reweighted L1-minimization," in Proc. Annu. Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, USA, Nov. 1-4, 2009, pp. 113-117.
53. 2 methods for finding sparse solutions," IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 317-329, Apr. 2010.
54. M. Khajehnejad, W. Xu, A. Avestimehr, and B. Hassibi, "Analyzing weighted l1 minimization for sparse recovery with nonuniform sparse models," IEEE Trans. Signal Process., vol. 59, no. 5, pp. 1985-2001, May 2011.
55. 1-minimization for compressed sensing," in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Kyoto, Japan, Mar. 25-30, 2012, pp. 3617-3620.
56. W. Xu and B. Hassibi, "Fundamental thresholds in compressed sensing: A high-dimensional geometry approach," in Compressive Sensing: Theory and Applications, Y. C. Eldar and G. Kutyniok, Eds. Cambridge, U.K.: Cambridge Univ. Press, Nov. 2012, ch. 7, pp. 305-347.
57. 1-norm minimization," IEEE Trans. Signal Process., vol. 61, no. 23, pp. 5905-5916, Dec. 2013.
58. C. Miosso et al., "Compressive sensing reconstruction with prior information by iteratively reweighted least-squares," IEEE Trans. Signal Process., vol. 57, no. 6, pp. 2424-2431, Jun. 2009.
59. rd Annu. CISS, Baltimore, MD, USA, Mar. 18-20, 2009, pp. 448-453.
60. R. Chartrand, "Exact reconstruction of sparse signals via nonconvex minimization,"IEEE Signal Process. Lett., vol. 14, no. 10, pp. 707-710, Oct. 2007.
61. R. Chartrand and Y. Wotao, "Iteratively reweighted algorithms for compressive sensing," in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Las Vegas, NV, USA, Mar. 31-Apr. 4, 2008, pp. 3869-3872.
62. Q. Ling and Z. Tian, "Decentralized sparse signal recovery for compressive sleeping wireless sensor networks," IEEE Trans. Signal Process., vol. 58, no. 7, pp. 3816-3827, Jul. 2010.
63. J. Berger, V. D. Oliveira, and B. Sanso, "Objective Bayesian analysis of spatially correlated data," J. Am. Statist. Assoc., vol. 96, pp. 1361-1374, Dec. 2000.
64. D. Zordan, G. Quer, M. Zorzi, and M. Rossi, "Modeling and generation of space-time correlated signals for sensor network fields," in Proc. IEEE Global Telecommun. Conf., Houston, TX, USA, Dec. 5-9, 2011, pp. 1-6.
65. M. A. Davenport, M. F. Duarte, Y. C. Eldar, and G. Kutyniok, "Introduction to compressed sensing," in Compressed Sensing: Theory and Applications, Y. C. Eldar and G. Kutyniok, Eds. Cambridge, U.K.: Cambridge Univ. Press, Nov. 2012, ch. 1, pp. 1-64.
66. E. Candes and T. Tao, "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005.
67. D. L. Donoho, M. Elad, and V. N. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise," IEEE Trans. Inf. Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006.
68. R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, "A simple proof of the restricted isometry property for random matrices," Constructive Approximation, vol. 28, no. 3, pp. 253-263, Dec. 2008.
69. E. Candes, "The restricted isometry property and its implications for compressed sensing," Comptes Rendus Math., vol. 346, no. 9/10, pp. 589-592, May 2008.
70. E. Candes and J. Romberg, "Sparsity and incoherence in compressive sampling," Inverse Problems, vol. 23, no. 3, pp. 969-985, Apr. 2007.
71. rd ed. New York, NY, USA: Academic, 2008.
72. R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, "Model-based compressive sensing," IEEE Trans. Inf. Theory, vol. 56, no. 4, pp. 1982-2001, Apr. 2010.
73. C. Caione, D. Brunelli, and L. Benini, "Distributed compressive sampling for lifetime optimization in dense wireless sensor networks," IEEE Trans. Ind. Informat., vol. 8, no. 1, pp. 30-40, Feb. 2012.
74. H. Mamaghanian, N. Khaled, D. Atienza, and P. Vandergheynst, "Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes," IEEE Trans. Biomed. Eng., vol. 58, no. 9, pp. 2456-2466, Sep. 2011.
75. th Int. Symp. Inf. Process. Sens. Netw., Apr. 2007, pp. 331-339.
76. R. Berinde and P. Indyk, "Sparse recovery using sparse random matrices,"MIT, Lexington, MA, USA, Apr. 2008.
77. th Annu. Allerton Conf. Commun., Control, Comput., Urbana-Champaign, IL, USA, Sep. 23-26, 2008, pp. 798-805.
78. rd Int. Conf. GeoSens. Netw., ser. GSN '09, Oxford, U.K., Jul. 13-14, 2009, pp. 11-20.
79. S. Lee, S. Pattem, M. Sathiamoorthy, B. Krishnamachari, and A. Ortega, "Compressed sensing and routing in sensor networks," Univ. Southern California, Los Angeles, CA, USA, Tech. Rep., 2009.
80. T. Do, L. Gan, N. Nguyen, and T. Tran, "Fast and efficient compressive sensing using structurally random matrices," IEEE Trans. Signal Process., vol. 60, no. 1, pp. 139-154, Jan. 2012.
81. J. Haupt and R. Nowak, "Adaptive sensing for sparse recovery,"in Compressive Sensing: Theory and Applications, Y. C. Eldar and G. Kutyniok, Eds. Cambridge, U.K.: Cambridge Univ. Press, Nov. 2012, ch. 6, pp. 269-304.
82. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
83. th Asilomar Signals, Syst. Comput., Pacific Grove, CA, USA, Nov. 7-10, 2010, pp. 808-812.
84. J. Ziniel and P. Schniter, "Dynamic compressive sensing of time-varying signals via approximate message passing," IEEE Trans. Signal Process., vol. 61, no. 21, pp. 5270-5284, Nov. 2013.
85. C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). Cambridge, MA, USA: MIT Press, 2005.
86. st ed. Providence, RI, USA: AMS, 2008.
87. 1-MAGIC: Recovery of Sparse Signals via Convex Programming, Oct. 2005. [Online]. Available: http://users.ece.gatech.edu/justin/l1magic/
88. M. Grant and S. Boyd, CVX: Matlab Software for Disciplined Convex Programming, Version 1.21, Apr. 2011. [Online]. Available: http://cvxr.com/cvx
89. D. Donoho, A. Maleki, and M. Shahram, "Wavelab 850: Wavelet toolbox for matlab," Jan. 2006. [Online]. Available: http://www-stat.stanford.edu/wavelab