Evolutionary Design of Message Efficient Secrecy Amplification Protocols

  • Tobiáš Smolka
  • Petr Švenda
  • Lukáš Sekanina
  • Vashek Matyáš
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7244)


Secrecy amplification protocols are mechanisms that can significantly improve security of partially compromised wireless sensor networks (e.g., turning a half-compromised network into the 95% secure one). The main disadvantage of existing protocols is a high communication overhead increasing exponentially with network density. We devise a novel family of these protocols exhibiting only a linear increase of the communication overhead. The protocols are automatically generated by linear genetic programming (LGP) connected to a network simulator. After a deep analysis of various characteristics of this new family of protocols, with a special focus on the tuning of LGP parameters, new and better group-oriented protocols are discovered by LGP. A multi-criteria optimization is then utilized to further reduce the communication overhead down to 1/2 of the original amount while maintaining the original fraction of secure links.


Wireless Sensor Network Communication Overhead Network Simulator Evolutionary Design Linear Genetic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, D.: Boinc: A system for public-resource computing and storage. In: Proceedings of Fifth IEEE/ACM International Workshop on Grid Computing, pp. 4–10. IEEE (2004)Google Scholar
  2. 2.
    Anderson, R., Chan, H., Perrig, A.: Key infection: Smart trust for smart dust. In: ICNP 2004, pp. 206–215. IEEE (2004)Google Scholar
  3. 3.
    Bernardi, P., Sánchez, E., Schillaci, M., Squillero, G., Reorda, M.S.: An effective technique for minimizing the cost of processor software-based diagnosis in socs. In: IEEE DATE 2006: Design, Automation and Test in Europe, pp. 412–417 (2006)Google Scholar
  4. 4.
    Brameier, M., Banzhaf, W.: Linear Genetic Programming. Springer, Berlin (2007)Google Scholar
  5. 5.
  6. 6.
    Cvrcek, D., Svenda, P.: Smart dust security-key infection revisited. Electronic Notes in Theoretical Computer Science 157(3), 11–25 (2006)CrossRefGoogle Scholar
  7. 7.
    Eschenauer, L., Gligor, V.: A key-management scheme for distributed sensor networks, pp. 41–47 (2002)Google Scholar
  8. 8.
    Švenda, P., Sekanina, L., Matyáš, V.: Evolutionary design of secrecy amplification protocols for wireless sensor networks. In: Proceedings of the Second ACM Conference on Wireless Network Security, pp. 225–236. ACM (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tobiáš Smolka
    • 1
  • Petr Švenda
    • 1
  • Lukáš Sekanina
    • 2
  • Vashek Matyáš
    • 1
  1. 1.Faculty of InformaticsMasaryk UniversityCzech Republic
  2. 2.FIT, IT4Innovations CentreBrno University of TechnologyBrnoCzech Republic

Personalised recommendations