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Applying the Concept of Artificial DNA and Hormone System to a Low-Performance Automotive Environment

  • Uwe BrinkschulteEmail author
  • Felix Fastnacht
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11479)

Abstract

Embedded systems are growing very complex because of the increasing chip integration density, larger number of chips in distributed applications and demanding application fields e.g. in autonomous cars. Bio-inspired techniques like self-organization are a key feature to handle the increasing complexity of embedded systems. In biology the structure and organization of a system is coded in its DNA, while dynamic control flows are regulated by the hormone system. We adapted these concepts to embedded systems using an artificial DNA (ADNA) and an artificial hormone system (AHS). Based on these concepts, highly reliable, robust and flexible systems can be created. These properties predestine the ADNA and AHS for the use in future automotive applications.

However, computational resources and communication bandwidth are often limited in automotive environments. Nevertheless, in this paper we show that the concept of ADNA and AHS can be successfully applied to an environment consisting of low-performance automotive microcontrollers interconnected by a classical CAN bus.

Keywords

Artificial DNA Artificial hormone system Self-organization Automotive environment CAN bus 

References

  1. 1.
    Bernauer, A., Bringmann, O., Rosenstiel, W.: Generic self-adaptation to reduce design effort for system-on-chip. In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), San Francisco, USA, pp. 126–135 (2009)Google Scholar
  2. 2.
    BMBF: Autokonf projekt. http://autokonf.de/
  3. 3.
    Bosch: CAN Specifications Version 2.0. http://esd.cs.ucr.edu/webres/can20.pdf
  4. 4.
    Brinkschulte, U., Müller-Schloer, C., Pacher, P. (eds.): Proceedings of the Workshop on Embedded Self-Organizing Systems, San Jose, USA (2013)Google Scholar
  5. 5.
    Brinkschulte, U.: Video of the KDNA controlled robot vehicle. http://www.es.cs.uni-frankfurt.de/index.php?id=252
  6. 6.
    Brinkschulte, U.: An artificial DNA for self-descripting and self-building embedded real-time systems. Pract. Exp. Concurr. Comput. 28, 3711–3729 (2015)CrossRefGoogle Scholar
  7. 7.
    Brinkschulte, U.: Prototypic implementation and evaluation of an artificial DNA for self-describing and self-building embedded systems. In: 19th IEEE International Symposium on Real-time Computing (ISORC 2016), York, UK, 17–20 May 2016Google Scholar
  8. 8.
    Brinkschulte, U.: Prototypic implementation and evaluation of an artificial DNA for self-descripting and self-building embedded systems. EURASIP J. Embed. Syst. (2017).  https://doi.org/10.1186/s13639-016-0066-2
  9. 9.
    Brinkschulte, U., Pacher, M., von Renteln, A.: An artificial hormone system for self-organizing real-time task allocation in organic middleware. In: Brinkschulte, U., Pacher, M., von Renteln, A. (eds.) Organic Computing. UCS, pp. 261–283. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-540-77657-4_12CrossRefGoogle Scholar
  10. 10.
    Garzon, M.H., Yan, H. (eds.): DNA 2007. LNCS, vol. 4848. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-77962-9CrossRefzbMATHGoogle Scholar
  11. 11.
    Yi, C.H., Kwon, K., Jeon, J.W.: Method of improved hardware redundancy for automotive system, pp. 204–207 (2015)Google Scholar
  12. 12.
    Hornby, G., Lipson, H., Pollack, J.: Evolution of generative design systems for modular physical robots. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2001, vol. 4, pp. 4146–4151 (2001)Google Scholar
  13. 13.
    IBM: Autonomic Computing (2003). http://www.research.ibm.com/autonomic/
  14. 14.
    Becker, J., et al.: Digital on-demand computing organism for real-time systems. In: Workshop on Parallel Systems and Algorithms (PASA), ARCS 2006, Frankfurt, Germany, March 2006Google Scholar
  15. 15.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Comput. 1, 41–50 (2003)CrossRefGoogle Scholar
  16. 16.
    Kluge, F., Mische, J., Uhrig, S., Ungerer, T.: CAR-SoC - towards and autonomic SoC node. In: Second International Summer School on Advanced Computer Architecture and Compilation for Embedded Systems (ACACES 2006), L’Aquila, Italy, July 2006Google Scholar
  17. 17.
    Kluge, F., Uhrig, S., Mische, J., Ungerer, T.: A two-layered management architecture for building adaptive real-time systems. In: Brinkschulte, U., Givargis, T., Russo, S. (eds.) SEUS 2008. LNCS, vol. 5287, pp. 126–137. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-87785-1_12CrossRefGoogle Scholar
  18. 18.
    Lawson, K.: Atomthreads: open source RTOS, free lightweight portable scheduler. https://atomthreads.com/
  19. 19.
    Lee, E., Neuendorffer, S., Wirthlin, M.: Actor-oriented design of embedded hardware and software systems. J. Circ. Syst. Comput. 12, 231–260 (2003)CrossRefGoogle Scholar
  20. 20.
    Lee, J.Y., Shin, S.Y., Park, T.H., Zhang, B.T.: Solving traveling salesman problems with dna molecules encoding numerical values. Biosystems 78(1–3), 39–47 (2004)CrossRefGoogle Scholar
  21. 21.
    Lipsa, G., Herkersdorf, A., Rosenstiel, W., Bringmann, O., Stechele, W.: Towards a framework and a design methodology for autonomic SoC. In: 2nd IEEE International Conference on Autonomic Computing, Seattle, USA (2005)Google Scholar
  22. 22.
    Maurer, M., Gerdes, J.C., Winner, B.L.H.: Autonomous Driving - Technical, Legal and Social Aspects. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-48847-8CrossRefGoogle Scholar
  23. 23.
    Nicolescu, G., Mosterman, P.J.: Model-Based Design for Embedded Systems. CRC Press, Boca Raton, London, New York (2010)Google Scholar
  24. 24.
    Renesas: V850E2/Px4 user manual. http://renesas.com/
  25. 25.
    Sangiovanni-Vincentelli, A., Martin, G.: Platform-based design and software design methodology for embedded systems. IEEE Des. Test 18(6), 23–33 (2001)CrossRefGoogle Scholar
  26. 26.
    Schmeck, H.: Organic computing - a new vision for distributed embedded systems. In: 8th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC 2005), pp. 201–203. Seattle, USA, May 2005Google Scholar
  27. 27.
    VDE/ITG (Hrsg.): VDE/ITG/GI-Positionspapier Organic Computing: Computer und Systemarchitektur im Jahr 2010. GI, ITG, VDE (2003)Google Scholar
  28. 28.
    Weiss, G., Zeller, M., Eilers, D., Knorr, R.: Towards self-organization in automotive embedded systems. In: González Nieto, J., Reif, W., Wang, G., Indulska, J. (eds.) ATC 2009. LNCS, vol. 5586, pp. 32–46. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-02704-8_4CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institut für InformatikGoethe Universität Frankfurt am MainFrankfurtGermany
  2. 2.Intedis GmbH & Co. KGWürzburgGermany

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