Skip to main content

Signal Processing Techniques for Energy Efficiency, Security, and Reliability in the IoT Domain

  • Chapter
  • First Online:
Internet of Things (IoT) in 5G Mobile Technologies

Abstract

The next generation of communication networks, known as 5G technologies, is envisioned to address several major technical challenges like increased data rates, efficient spectral use, higher capacity, etc. One of the core pillars of the 5G technologies is the Internet of Things (IoT) use-case. This employs hundreds or even thousands of smart objects serving numerous applications (e.g. environmental monitoring, smart homes, smart traffic management, etc.). Typical IoT applications become feasible through the use of large-scale Wireless Sensor Networks deployed using a number of miniature devices called as sensors or motes. In this chapter, we demonstrate how two popular signal processing techniques, namely Compressive Sensing and Matrix Completion can be used to make feasible energy efficiency, lightweight encryption, and packet loss mitigation. Furthermore, we present an IoT platform based on a Software Defined Radio that provides multiple channel support for both IEEE 802.11 and IEEE 802.15.4 standards.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://db.csail.mit.edu/labdata/labdata.html.

References

  1. IEEE 802.11 Working Group and others, IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std 802.11-2007 (Revision of IEEE Std 802.11-1999), p. C1 (2009)

    Google Scholar 

  2. Siris, V., Stamatakis, G., Tragos, E.: A simple end-to-end throughput model for 802.11 multi-radio multi-rate wireless mesh networks. IEEE Commun. Lett. 635–637 (2011)

    Google Scholar 

  3. Chaari, L., Kamoun, L.: Performance analysis of ieee 802.15.4/zigbee standard under real time constraints. Int. J. Comput. Netw. Commun. 3, 235–251 (2011)

    Google Scholar 

  4. Candes, E., Wakin, M.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)

    Article  Google Scholar 

  5. Tropp, J., Gilbert, A.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53, 4655–4666 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Candes, E., Plan, Y.: Matrix completion with noise. Proc. IEEE 98(6), 925–936 (2010)

    Article  Google Scholar 

  7. Candes, E., Recht, B.: Exact matrix completion via convex optimization. Commun. ACM 55(6), 111–119 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. Eisenberg, M.: Hill ciphers and modular linear algebra. University of Massachusetts, Mimeographed notes (1998)

    Google Scholar 

  9. Rachlin, Y., Baron, D.: The secrecy of compressed sensing measurements. In: Proceedings of Allerton Conference on Communication, Control, and Computing, pp. 813–817 (2008)

    Google Scholar 

  10. Orsdemir, A., Altun, H., Sharma, G., Bocko, M.: On the security and robustness of encryption via compressed sensing. In: Proceedings of MILCOM, pp. 1–7 (2008)

    Google Scholar 

  11. Shannon, C.: Communication theory of secrecy systems. Bell Syst. Tech. J 28, 656–715 (1949)

    Article  MathSciNet  MATH  Google Scholar 

  12. Fragkiadakis, A., Tragos, E., Traganitis, A.: Lightweight and secure encryption using channel measurements. In: Proceedings of Vitae, pp. 1–5 (2014)

    Google Scholar 

  13. Premnath, S., Jana, S., Croft, J., Gowda, P., Clark, M., Kasera, S., Patwari, N., Krishnamurthy, S.: Secret key extraction from wireless signal strength in real environments. IEEE Trans. Mob. Comput. 12(5), 917–930 (2013)

    Article  Google Scholar 

  14. Jakes, W.: Microwave Mobile Communications. Wiley (1974)

    Google Scholar 

  15. Ye, C., Reznik, A., Shah, Y.: Extracting secrecy from jointly gaussian random variables. In: Proceedings of ISIT, pp. 2593–2597 (2006)

    Google Scholar 

  16. Aumassony, J., Henzenz, L., Plasencia, W.M.M.: Quark: a lightweight hash. J. Cryptol. 26(2), 313–339 (2013)

    Article  MathSciNet  Google Scholar 

  17. Dodis, Y., Ostrovsky, R., Reyzin, L., Smith, A.: Fuzzy extractors: how to generate strong keys from biometrics and other noisy data. SIAM J. Comput. 38(1), 97–139 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Sensorscope: Sensor networks for environmental monitoring. http://lcav.epfl.ch/sensorscope-en

  19. Tragos, E., Fragkiadakis, A., Askoxylakis, I., Siris, V.: The impact of interference on the performance of a multi-path metropolitan wireless mesh network. In: Proceedings of ISCC, pp. 199–204 (2011)

    Google Scholar 

  20. The open source os for the internet of things. http://www.contiki-os.org

  21. Osterlind, F., Dunkels, A., Eriksson, J., Finne, N., Voigt, T.: Cross-level sensor network simulation with cooja. In: Proceedings of 31st IEEE Conference on Local Computer Networks, pp. 641–648 (2006)

    Google Scholar 

  22. Cai, J., Candes, E., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20, 1956–1982 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  23. Malioutov, D.M., Sanghavi, S.R., Willsky, A.S.: Sequential compressed sensing. IEEE J. Sel. Top. Sign. Proces. 4(2), 435–444 (2010)

    Article  Google Scholar 

  24. Boufounos, P., Duarte, M.F., Baraniuk, R.G.: Sparse signal reconstruction from noisy compressive measurements using cross validation. In: IEEE/SP 14th Workshop on Statistical Signal Processing SSP’07, pp. 299–303. IEEE (2007)

    Google Scholar 

  25. Chen, W., Wassell, I.: Energy efficient signal acquisition via compressive sensing in wireless sensor networks. In: Proceedings of ISWPC (2011)

    Google Scholar 

  26. Wang, J., Tang, S., Yin, B., Li, X.: Data gathering in wireless sensor networks through intelligent compressive sensing. In: Proceedings of Infocom, pp. 603–611 (2012)

    Google Scholar 

  27. Shen, Y., Hu, W., Rana, R., Chou, C.: Non-uniform compressive sensing in wireless sensor networks: feasibility and application. In: Proceedings of ISSNIP, pp. 271–276 (2011)

    Google Scholar 

  28. Fragkiadakis, A., Charalampidis, P., Papadakis, S., Tragos, E.: Experiences with deploying compressive sensing and matrix completion techniques in iot devices. In: Proceedings of CAMAD, pp. 213–217 (2014)

    Google Scholar 

  29. Fragkiadakis, A., Nikitaki, S., Tsakalides, P.: Physical-layer intrusion detection for wireless networks using compressed sensing. In: Proceedings of WiMob, pp. 845–852 (2012)

    Google Scholar 

  30. Do, T., Gan, L., Nguyen, N., Tran, T.: Fast and efficient compressive sensing using structurally random matrices. IEEE Trans. Signal Process. 60(1), 139–154 (2012)

    Google Scholar 

  31. Donoho, D., Tanner, J.: Precise undersampling theorems. Proceedings of the IEEE 98(6), 913–924 (2010)

    Google Scholar 

  32. Ward, R.: Compressed sensing with cross validation. IEEE Trans. Inf. Theory 55(12), 5773–5782 (2009)

    Article  MathSciNet  Google Scholar 

  33. GNU Radio. http://gnuradio.org

  34. Yeo, E., Augsburger, S., Davis, W., Nikolic, B.: A 500-mb/s soft-output viterbi decoder. IEEE J. Solid-State Circuits 38, 1234–1241 (2003)

    Article  Google Scholar 

  35. Schmid, T.: GNU Radio 802.15.4 En- and Decoding (2006)

    Google Scholar 

  36. Linux wireless. http://wireless.kernel.org

  37. 6LoWPAN Linux implementation. https://www.kernel.org/doc/Documentation/networking/ieee802154.txt

  38. Tragos, E., Angelakis, V.: Cognitive radio inspired m2m communications. In: Proceedings of WPMC, pp. 1–5 (2013)

    Google Scholar 

Download references

Acknowledgments

This work has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under the grant agreements no 609094 and 612361.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexandros Fragkiadakis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Fragkiadakis, A., Tragos, E., Makrogiannakis, A., Papadakis, S., Charalampidis, P., Surligas, M. (2016). Signal Processing Techniques for Energy Efficiency, Security, and Reliability in the IoT Domain. In: Mavromoustakis, C., Mastorakis, G., Batalla, J. (eds) Internet of Things (IoT) in 5G Mobile Technologies. Modeling and Optimization in Science and Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-30913-2_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30913-2_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30911-8

  • Online ISBN: 978-3-319-30913-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics