Wireless Personal Communications

, Volume 104, Issue 2, pp 577–593 | Cite as

Lightweight Security Scheme for Internet of Things

  • Ahmed Aziz
  • Karan SinghEmail author


The compressive sensing method presents itself as a promising technique in many fields specially for the Internet of Things and Wireless sensor networks applications. That is because, the compressive sensing has the major advantage of performing lightweight encryption and compression simultaneously. It leads to secure the network in addition to prolong the network life time. However, chosen plaintext attacks and key distribution are still major challenges facing the compressive sensing method. This paper focuses on the compressive sensing method according to security issue, and propose an efficient lightweight security scheme that addressee the previous challenges. Moreover, we use experimental data collected from a real sensors located in Intel Berkeley Research Lab.


Wireless sensor networks Internet of Things Compressive sensing Security 



  1. 1.
    Chen, L., Thombre, S., Jarvinen, K., Lohan, E. S., Alen-Savikko, A. K., Leppakoski, H., et al. (2017). Robustness, security and privacy in location-based services for future IoT: A survey. IEEE Access, 5(99), 1.Google Scholar
  2. 2.
    Palopoli, L., Passerone, R., & Rizano, T. (2011). Scalable offline optimization of industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 7(2), 328–329.CrossRefGoogle Scholar
  3. 3.
    Mollin, R. A. (2006). An introduction to cryptography. Boca Raton: CRC Press.zbMATHGoogle Scholar
  4. 4.
    Vanstone, S. A., Menezes, A. J., & Oorschot, P. C. (1999). Handbook of applied cryptography. Boca Raton: CRC Press.zbMATHGoogle Scholar
  5. 5.
    Fragkiadakis, A., Tragos, E., & Traganitis, A. (2014). Lightweight and secure encryption using channel measurements. In 4th international conference on wireless communications, vehicular technology, information theory and aerospace, Aalborg (pp. 1–5).Google Scholar
  6. 6.
    Donoho, D. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Candes, E. J., & Tao, T. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies. IEEE Transactions on Information Theory, 52(12), 5406–5425.MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Cossalter, M., Valenzise, G., Tagliasacchi, M., & Tubaro, S. (2010). Joint compressive video coding and analysis. IEEE Transactions on Multimedia, 12(3), 168183.CrossRefGoogle Scholar
  9. 9.
    Premnath, S., Jana, S., Croft, J., Gowda, P., Clark, M., Kasera, S., et al. (2013). Secret key extraction from wireless signal strength in real environments. IEEE Transactions on Mobile Computing, 12(5), 917–930.CrossRefGoogle Scholar
  10. 10.
    Li, Z., Xu, W., Miller, R., & Trappe, W. (2006). Securing wireless systems via lower layer enforcements, In Proceedings of the WiSe (pp. 33–42).Google Scholar
  11. 11.
    Dautov, R., & Tsouri, G. (2013). Establishing secure measurement matrix for compressed sensing using wireless physical layer security. In Proceedings of ICNC (pp. 354–358).Google Scholar
  12. 12.
    Fragkiadakis, L., Tragos, E., Makrogiannakis, A., Papadakis, S., Charalampidis, P., & Surligas, M. (2016). Signal processing techniques for energy efficiency, security, and reliability in the IoT domain (pp. 19–447). New York: Springer.Google Scholar
  13. 13.
    Rachlin, Y., & Baron, D. (2008). The secrecy of compressed sensing measurements. In Proceedings of 46th annual Allerton conference on communication, control, and computing (pp. 813–817).Google Scholar
  14. 14.
    Cambareri, V., Mangia, M., Pareschi, F., Rovatti, R., & Setti, G. (2015). Low-complexity multiclass encryption by compressed sensing. IEEE Transactions on Signal Processing, 63, 21832195.MathSciNetzbMATHGoogle Scholar
  15. 15.
    Yu, L., Barbot, J. P., Zheng, G., & Sun, H. (2010). Compressive sensing with chaotic sequence. IEEE Signal Processing Letters, 17(8), 731734.Google Scholar
  16. 16.
    Zhang, L., Wong, K., Li, C., & Zhang, Y. (2014). Towards secure compressive sampling scheme. CoRR.Google Scholar
  17. 17.
    Fragkiadakis, A., Kovacevic, L., & Tragos, E. (2016). Enhancing compressive sensing encryption in constrained devices using chaotic sequences. In Proceedings of the 2nd workshop on experiences in the design and implementation of smart objects (pp. 17–22).Google Scholar
  18. 18.
    Liu, H., Zhu, Z., Jiang, H., & Wang, B. (2008). A novel image encryption algorithm based on improved 3D Chaotic Cat Map. In The 9th international conference for young computer scientists.Google Scholar
  19. 19.
    Candes, E., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(52), 145–156.MathSciNetzbMATHGoogle Scholar
  20. 20.
    Baraniuk, R. G. (2007). Compressive sensing. IEEE Signal Processing Magazine, 24(4), 118–121.CrossRefGoogle Scholar
  21. 21.
    Mallat, S. (1999). A wavelet tour of signal processing. Cambridge: Academic Press.zbMATHGoogle Scholar
  22. 22.
    Venkataramani, R., & Bresler, Y. (1998). Sub-nyquist sampling of multiband signals: Perfect reconstruction and bounds on aliasing error. In IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 12–15).Google Scholar
  23. 23.
    Tropp, J., & Gilber, A. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(14), 4655–4666.MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Donoho, D., Yaakov, T., Drori, I., & Jean, S. (2012). Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Transactions on Information Theory, 58(2), 1094–1121.MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Deanna, N., & Roman, V. (2009). Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Foundations of Computational Mathematics, 9(3), 317–334.MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Luo, C., Wu, F., Sun, J., & Chen, C. W.(2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on mobile computing and networking, MobiCom 09 (pp. 145–156), New York, NY, USA.Google Scholar
  27. 27.
    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference (pp. 3005–3014).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and System ScienceJawaharlal Nehru UniversityNew DelhiIndia
  2. 2.Benha UniversityBenhaEgypt

Personalised recommendations