Skip to main content
SpringerLink
Log in

A Distributed Secure Data Collection Scheme via Chaotic Compressed Sensing in Wireless Sensor Networks

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Motivated by chaos technology and compressed sensing, we propose a distributed secure data collection scheme via chaotic compressed sensing in wireless sensor networks. The chaotic compressed sensing is applied to the encrypted compression of sensory data for sensor node and the data acquisition for whole sensory in wireless sensor networks. The proposed scheme is suitable for long-term and large scale wireless sensor networks with energy efficiency, network lifetime and security. A sensing matrix generation algorithm and active node matrix algorithm based on chaos sequence are proposed to ensure the secure and efficient transmission of sensor packets. The secret key crack, forgery, hijack jamming and replay attacks on the proposed algorithm are evaluated to show the robustness of this scheme. Simulations and real data examples are also given to show that the proposed scheme can ensure the secure data acquisition in wireless sensor networks efficiently.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. A.M. Abdulghani, E. Rodriguez-Villegas, Compressive sensing: from “compressing while sampling” to “compressing and securing while sampling”, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2010), pp. 1127–1130

    Chapter  Google Scholar 

  2. C.L. Barrett, S.J. Eidenbenz, L. Kroc, M. Marathe, J.P. Smith, Parametric probabilistic routing in sensor networks. Mob. Netw. Appl. 10(4), 529–544 (2005)

    Article  Google Scholar 

  3. S. Boccaletti, C. Grebogi, Y.C. Lai, H. Mancini, D. Maza, The control of chaos: theory and applications. Phys. Rep. 329(3), 103–197 (2000)

    Article  MathSciNet  Google Scholar 

  4. E.J. Candes, T. Tao, Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52(12), 5406–5425 (2006)

    Article  MathSciNet  Google Scholar 

  5. X. Chen, K. Makki, K. Yen, N. Pissinou, Sensor network security: a survey. IEEE Commun. Surv. Tutor. 11(2), 52–73 (2009)

    Article  Google Scholar 

  6. D. Ebdon, Statistics in Geography (Blackwell, Oxford, 1985)

    Google Scholar 

  7. V.P. Dinh, L.T. Nguyen, D.T. Tran, V.L. Ha, M.N. Do, Fast image collection in magnetic resonance imaging by chaotic compressed sensing, in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2011), pp. 85–88

    Google Scholar 

  8. D. Djenouri, L. Khelladi, A.N. Badache, A survey of security issues in mobile ad hoc and sensor networks. IEEE Commun. Surv. Tutor. 7(2), 2–28 (2005)

    Article  Google Scholar 

  9. F. Fazel, M. Fazel, M. Stojanovic, Random access compressed sensing for energy-efficient underwater sensor networks. IEEE J. Sel. Areas Commun. 29(8), 660–1670 (2011)

    Article  Google Scholar 

  10. X.Y. He, R.F. Song, W.P. Zhu, Optimal pilot pattern design for compressed sensing-based sparse channel estimation in OFDM systems. Circuits Syst. Signal Process. 31(4), 1379–1395 (2012)

    Article  MathSciNet  Google Scholar 

  11. G. Kaddoum, A.J. Lawrance, P. Charge, Chaos communication performance: theory and computation. Circuits Syst. Signal Process. 30(1), 185–208 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  12. G. Kaddoum, S. Gagne, F. Gagnon, Removing cyclostationary properties in a chaos-based communication system. Circuits Syst. Signal Process. 30(6), 1391–1400 (2011)

    Article  MathSciNet  Google Scholar 

  13. Y.W. Law, G. Moniava, Z. Gong, P. Hartel, M. Palaniswami, KALwEN: a new practical and interoperable key management scheme for body sensor networks. Secur. Commun. Netw. 4(11), 1309–1329 (2010)

    Article  Google Scholar 

  14. P.P.C. Lee, V. Misra, D. Rubenstein, Distributed algorithms for secure multipath routing, in IEEE INFOCOM 2005: The Conference on Computer Communications (2005), pp. 1952–1963

    Google Scholar 

  15. Q. Ling, Z. Tian, Decentralized sparse signal recovery for compressive sleeping wireless sensor networks. IEEE Trans. Signal Process. 58(7), 3816–3827 (2010)

    Article  MathSciNet  Google Scholar 

  16. C. Luo, F. Wu, J. Sun, C.W. Chen, Compressive data gathering for large-scale wireless sensor networks, in Fifteenth ACM International Conference on Mobile Computing and Networking (2009), pp. 145–156

    Google Scholar 

  17. J. Luo, L. Xiang L, C. Rosenberg, Does compressed sensing improve the throughput of wireless sensor networks? in 2010 IEEE International Conference on Communications (2010), pp. 1–6

    Chapter  Google Scholar 

  18. S. Malladi, J. Alves-Foss, R. Heckendorn, On preventing replay attacks on security protocols, in International Conference on Security and Management (2002), pp. 77–83

    Google Scholar 

  19. C. Poepper, M. Strasser, S. Capkun, Anti-jamming broadcast communication using uncoordinated spread spectrum techniques. IEEE J. Sel. Areas Commun. 28(5), 703–715 (2010)

    Article  Google Scholar 

  20. R.O. Preda, D.N. Vizireanu, A robust digital watermarking scheme for video copyright protection in the wavelet domain. Measurement 43(10), 1720–1726 (2010)

    Article  Google Scholar 

  21. R.O. Preda, D.N. Vizireanu, Quantisation-based video watermarking in the wavelet domain with spatial and temporal redundancy. Int. J. Electron. 98(3), 393–405 (2011)

    Article  Google Scholar 

  22. R. Roman R, C. Alcaraz, J. Lopez, N. Sklavos, Key management systems for sensor networks in the context of the Internet of things. Comput. Electr. Eng. 37(2), 147–159 (2011)

    Article  Google Scholar 

  23. T. Shu, M. Krunz, S. Liu, Secure data collection in wireless sensor networks using randomized dispersive routes. IEEE Trans. Mob. Comput. 9(7), 941–954 (2010)

    Article  Google Scholar 

  24. D.N. Vizireanu, A fast, simple and accurate time-varying frequency estimation method for single-phase electric power systems. Measurement 45(5), 1331–1333 (2012)

    Article  Google Scholar 

  25. D.N. Vizireanu, S.V. Halunga, Simple, fast and accurate eight points amplitude estimation method of sinusoidal signals for DSP based instrumentation. J. Instrum. 7(4), 1–11 (2012)

    Article  Google Scholar 

  26. W. Wang, M. Garofalakis, K. Ramchandran, Distributed sparse random projections for refinable approximation, in Proceedings of the Sixth International Symposium on Information Processing in Sensor Networks (2007), pp. 331–339

    Chapter  Google Scholar 

  27. L. Yu, J.P. Barbot, G. Zheng, H. Sun, Compressive sensing with chaotic sequence. IEEE Signal Process. Lett. 17(8), 731–734 (2010)

    Article  Google Scholar 

  28. H. Zhang, Y. Shi, A.S. Mehr, Robust H∞ PID control for multivariable networked control systems with disturbance/noise attenuation. Int. J. Robust Nonlinear Control 22(2), 183–204 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  29. H. Zhang, Y. Shi, A.S. Mehr, Robust weighted H∞ filtering for networked systems with intermitted measurements of multiple sensors. Int. J. Adapt. Control Signal Process. 25(4), 313–330 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  30. http://www.cencoos.org/

Download references

Acknowledgements

This work is supported by National NSFC 60802009, China National Science and Technology Major Project 2013ZX03003-002-04 and 2010ZX03003-001-02, Sino-Korea International Cooperation Project 2012DFG12250, and Key Laboratory of Universal Wireless Communications Foundation Project. The work is also supported by China–EU International Scientific and Technological Cooperation Program (0902).

Author information

Authors and Affiliations

  1. Dept. of Elec. and Information Engn. at Huazhong Univ. of Sci. and Technol., Wuhan National Laboratory for Optoelectronics, Wuhan, China

    Wei Lu, Yingzhuang Liu & Desheng Wang

Authors
  1. Wei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingzhuang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Desheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Desheng Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lu, W., Liu, Y. & Wang, D. A Distributed Secure Data Collection Scheme via Chaotic Compressed Sensing in Wireless Sensor Networks. Circuits Syst Signal Process 32, 1363–1387 (2013). https://doi.org/10.1007/s00034-012-9516-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-012-9516-9

Keywords

Search

Navigation