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Mobile Information Systems
Volume 2016 (2016), Article ID 1763416, 9 pages
http://dx.doi.org/10.1155/2016/1763416
Research Article

A Perturbed Compressed Sensing Protocol for Crowd Sensing

Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Received 10 December 2015; Revised 28 April 2016; Accepted 10 May 2016

Academic Editor: Tony T. Luo

Copyright © 2016 Zijian Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. L. Palopoli, R. Passerone, and T. Rizano, “Scalable offline optimization of industrial wireless sensor networks,” IEEE Transactions on Industrial Informatics, vol. 7, no. 2, pp. 328–339, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Haupt, W. U. Bajwa, M. Rabbat, and R. Nowak, “Compressed sensing for networked data,” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 92–101, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Bobin, J.-L. Starck, and R. Ottensamer, “Compressed sensing in astronomy,” IEEE Journal on Selected Topics in Signal Processing, vol. 2, no. 5, pp. 718–726, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Mamaghanian, N. Khaled, D. Atienza, and P. Vandergheynst, “Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, pp. 2456–2466, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. YZ, Design and research on CS-based wireless sensor network spatial sparse signals network models [Ph.D. thesis], Nankai University, 2012.
  8. A. Orsdemir, H. O. Altun, G. Sharma, and M. F. Bocko, “On the security and robustness of encryption via compressed sensing,” in Proceedings of the IEEE Military Communications Conference (MILCOM '08), pp. 1–7, San Diego, Calif, USA, November 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Rachlin and R. D. Baron, “The secrecy of compressed sensing measurements,” in Proceedings of the 46th Annual Allerton Conference on Communication, Control, and Computing, pp. 813–817, IEEE, Urbana, Ill, USA, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. A. M. Abdulghani and E. Rodriguez-Villegas, “Compressive sensing: from ‘compressing while sampling’ to ‘compressing and securing while sampling’,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '10), pp. 1127–1130, Buenos Aires, Argentina, September 2010.
  11. R. Dautov and G. R. Tsouri, “Establishing secure measurement matrix for compressed sensing using wireless physical layer security,” in Proceedings of the International Conference on Computing, Networking and Communications (ICNC '13), pp. 354–358, San Diego, Calif, USA, January 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Zhang, M. M. Kermani, A. Raghunathan, and N. K. Jha, “Energy-efficient and secure sensor data transmission using encompression,” in Proceedings of the 26th International Conference on VLSI Design and 12th International Conference on Embedded Systems (ES '13), pp. 31–36, IEEE, Pune, India, January 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. http://www.select.cs.cmu.edu/data/labapp3/index.html.
  14. http://tao.ndbc.noaa.gov/.
  15. S.-T. Li and D. Wei, “A survey on compressive sensing,” Acta Automatica Sinica, vol. 35, no. 11, pp. 1369–1377, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Cohen, W. Dahmen, and R. DeVore, “Compressed sensing and best k-term approximation,” Journal of the American Mathematical Society, vol. 22, no. 1, pp. 211–231, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. E. J. Candès, “Compressive sampling,” in Proceedings of the International Congress of Mathematicians, vol. 3, pp. 1433–1452, Madrid, Spain, August 2006.
  18. E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Communications on Pure and Applied Mathematics, vol. 59, no. 8, pp. 1207–1223, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: universal encoding strategies?” IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406–5425, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. E. J. Candès, Y. C. Eldar, D. Needell, and P. Randall, “Compressed sensing with coherent and redundant dictionaries,” Applied and Computational Harmonic Analysis, vol. 31, no. 1, pp. 59–73, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. S. G. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397–3415, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655–4666, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. D. L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, “Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 1094–1121, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. D. Needell and R. Vershynin, “Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit,” Foundations of Computational Mathematics, vol. 9, no. 3, pp. 317–334, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Review, vol. 43, no. 1, pp. 129–159, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  26. S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An interior-point method for large-scale 1-regularized least squares,” IEEE Journal on Selected Topics in Signal Processing, vol. 1, no. 4, pp. 606–617, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE Journal on Selected Topics in Signal Processing, vol. 1, no. 4, pp. 586–597, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Communications on Pure and Applied Mathematics, vol. 57, no. 11, pp. 1413–1457, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  29. A. C. Gilbert, S. Guha, P. Indyk, S. Muthukrishnan, and M. Strauss, “Near-optimal sparse fourier representations via sampling,” in Proceedings of the 34th Annual ACM Symposium on Theory of Computing (STOC '02), pp. 152–161, ACM, 2002. View at Publisher · View at Google Scholar · View at MathSciNet
  30. A. C. Gilbert, M. J. Strauss, and R. Vershynin, “One sketch for all: fast algorithms for compressed sensing,” in Proceedings of the 39th ACM Symosium on the Theory of Computing (STOC '07), pp. 237–246, San Diego, Calif, USA, June 2007. View at Publisher · View at Google Scholar
  31. A. M. Abdulghani and E. Rodriguez-Villegas, “Compressive sensing: from ‘compressing while sampling’ to ‘compressing and securing while sampling’,” Proceedings of the 32rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1127–1130, 2010. View at Google Scholar · View at Scopus
  32. Y. Rachlin and R. D. Baron, “The secrecy of compressed sensing measurements,” in Proceedings of the 46th Annual Allerton Conference on Communication, Control, and Computing, pp. 813–817, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. R. Dautov and G. R. Tsouri, “Establishing secure measurement matrix for compressed sensing using wireless physical layer security,” in Proceedings of the 10th International Conference on Computing, Networking and Communications (ICNC '13), pp. 354–358, San Diego, Calif, USA, January 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. Y. Tsaig and D. L. Donoho, “Extensions of compressed sensing,” Signal Processing, vol. 86, no. 3, pp. 549–571, 2006. View at Publisher · View at Google Scholar
  35. B. Przydatek, D. Song, and A. Perrig, “SIA: secure information aggregation in sensor networks,” in Proceedings of the 1st ACM International Conference on Embedded Networked Sensor Systems (SenSys '03), pp. 255–265, November 2003. View at Scopus
  36. X.-Y. Liu, Y. Zhu, L. Kong et al., “CDC: compressive data collection for wireless sensor networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 8, pp. 2188–2197, 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967. View at Publisher · View at Google Scholar
  38. C. Luo, F. Wu, J. Sun, and C. W. Chen, “Compressive data gathering for large-scale wireless sensor networks,” in Proceedings of the 15th Annual ACM International Conference on Mobile Computing and Networking (MobiCom '09), pp. 145–156, ACM, Beijing, China, September 2009. View at Publisher · View at Google Scholar · View at Scopus