Evolutionary Intelligence

, Volume 1, Issue 2, pp 113–132 | Cite as

Improving the reliability of real-time embedded systems using innate immune techniques

  • Nicholas Lay
  • Iain Bate
Research Paper


Previous work has shown that immune-inspired techniques have good potential for solving problems associated with the development of real-time embedded systems (RTES), where for various reasons traditional real-time development techniques are not suitable. This paper examines in more detail the general applicability of the Dendritic Cell Algorithm (DCA) to the problem of task scheduling in RTES. To make this possible, an understanding of the problem characteristics is formalised, such that the results produced by the DCA can be examined in relation to the overall problem difficulty. The paper then contains a detailed understanding of how well the DCA which demonstrates that it generally performs well, however it clearly identifies properties of anomalies that are difficult to detect. These properties are as anticipated based on real-time scheduling theory.


Artificial immune systems Dendritic cell algorithm Real-time systems Embedded systems Task scheduling 



This work is supported by EPSRC and Philips Research through a CASE award, reference CASE/CNA/04/78.


  1. 1.
    Burns A, Wellings A (2001) Real-time systems and programming languages, 3rd edn, Pearson Education, Upper Saddle RiverGoogle Scholar
  2. 2.
    Graham RL (1969) Bounds on multiprocessing timing anomalies. SIAM J Appl Math 17:416–429zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Lundqvist T, Stenstrom P (1999) Timing anomalies in dynamically scheduled microprocessors. In: Proceedings of 20th IEEE real-time systems symposium (RTSS) 1999, pp 12–21Google Scholar
  4. 4.
    Engblom J (2003) Analysis of the execution time unpredictability caused by dynamic branch prediction. In: Proceedings of 9th real-time and embedded technology and applications symposium (RTAS) 2003, pp 152–159Google Scholar
  5. 5.
    Holsti N, Saarinen S (2002) Status of the Bound-T WCET tool, Proc 2nd international workshop on worst-case execution time analysisGoogle Scholar
  6. 6.
    Theiling H, Ferdinand C, Wilhelm R (2000) Fast and precise WCET prediction by separated cache and path analyses. Real-Time Syst 18(2):157CrossRefGoogle Scholar
  7. 7.
    Lay N, Bate I (2007) Applying artificial immune systems to real-time embedded systems. In: Proceedings of the congress on evolutionary computation (CEC) 2007, pp 3743–3750Google Scholar
  8. 8.
    Greensmith J, Aickelin U, Twycross J (2006) Articulation and clarification of the dendritic cell algorithm. In: Proceedings of international conference on artificial immune systems (ICARIS) 2006, pp 404–417Google Scholar
  9. 9.
    Puschner P, Burns A (2000) Guest editorial: a review of worst-case execution-time analysis. Real-Time Syst 18(2):115–128CrossRefGoogle Scholar
  10. 10.
    Bouyssounouse B, Sifakis J (2005) Embedded systems design: the ARTIST roadmap for research and development. LNCS, vol 3436. Springer, BerlinGoogle Scholar
  11. 11.
    Graaf B, Lormans M, Toetenel H (2003) Embedded software engineering: the state of the practice. IEEE Softw 20(6):61–69CrossRefGoogle Scholar
  12. 12.
    Vahid F, Givargis TD (2002) Embedded system design: a unified hardware/software introduction. Wiley, New YorkGoogle Scholar
  13. 13.
    Eisenring M, Thiele L, Zitzler E (2000) Conflicting criteria in embedded system design. IEEE Des Test Comput 17(2):51CrossRefGoogle Scholar
  14. 14.
    Hart E, Ross P, Nelson J (1998) Producing robust schedules via an artificial immune system. In: Proceedings of the world congress on computational intelligence (WCCI) 1998, pp 464–469Google Scholar
  15. 15.
    Hart E, Ross P (1999) An immune system approach to scheduling in changing environments. In: Proceedings of genetic and evolutionary computation conference (GECCO) 1999, pp 1559–1566Google Scholar
  16. 16.
    Colin A, Petters SM (2003) Experimental evaluation of code properties for WCET analysis. In: Proceedings of 24th IEEE real-time systems symposium (RTSS) 2003, pp 190–199Google Scholar
  17. 17.
    Bernat G, Colin A, Petters SM (2002) WCET analysis of probabilistic hard real-time systems. In: Proceedings of 23rd real-time systems symposium (RTSS) 2002, pp 279–288Google Scholar
  18. 18.
    Audsley NC, Burns A, Richardson MF, Wellings AJ (1991) Hard real-time scheduling: the deadline monotonic approach. In: Proceedings 8th IEEE workshop on real-time operating systems and software 1991, pp 133–137Google Scholar
  19. 19.
    Sha L, Rajkumar R, Sathaye SS (1994) Generalized rate-monotonic scheduling theory: a framework for developing real-time systems. Proc IEEE 82(1):68–82CrossRefGoogle Scholar
  20. 20.
    Liu CL, Layland JW (1973) Scheduling algorithms for multiprogramming in a hard-real-time environment. J ACM 20(1):40–61CrossRefMathSciNetGoogle Scholar
  21. 21.
    Katcher DI, Arakawa H, Strosnider JK (1993) Engineering and analysis of fixed priority schedulers. IEEE Trans Softw Eng 19(9):920–934CrossRefGoogle Scholar
  22. 22.
    Harter PK Jr (1987) Response times in level-structured systems. ACM Trans Comput Syst 5(3):232–248CrossRefGoogle Scholar
  23. 23.
    Buttazzo GC, Sensini F (1999) Optimal deadline assignment for scheduling soft aperiodic tasks in hard real-time environments. Trans Comput 48(10):1035CrossRefGoogle Scholar
  24. 24.
    Stepney S, Smith R, Timmis J, Tyrrell A, Neal M, Hone A (2005) Conceptual frameworks for artificial immune systems. Int J Unconv Comput 1(3):315–338Google Scholar
  25. 25.
    de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, BerlinzbMATHGoogle Scholar
  26. 26.
    Hart E, Ross P (1999) The evolution and analysis of potential antibody library for use in job-shop scheduling. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimisation. McGraw-Hill, New York, pp 185–202Google Scholar
  27. 27.
    Medzhitov R, Janeway CA Jr (1998) Innate immune recognition and control of adaptive immune responses. Semin Immunol 10(5):351–353CrossRefGoogle Scholar
  28. 28.
    Twycross J, Aickelin U (2005) Towards a conceptual framework for innate immunity. In: Proceedings of international conference on artificial immune systems (ICARIS) 2005, pp 112–125Google Scholar
  29. 29.
    Neal M, Feyereisl J, Rascunà R, Wang X (2006) Don’t touch me, I’m fine: robot autonomy using an artificial innate immune system. In: Proceedings of international conference on artificial immune systems (ICARIS) 2006, pp 349–361Google Scholar
  30. 30.
    Zhang X, Dragffy G, Pipe AG, Zhu QM (2005) Artificial innate immune system: An instant defence layer of embryonics. In: Proceedings of international conference on artificial immune systems (ICARIS) 2005, pp 302–315Google Scholar
  31. 31.
    Burnet FM (1968) Evolution of the immune process in vertebrates. Nature 218:426–430CrossRefGoogle Scholar
  32. 32.
    Stibor T, Mohr P, Timmis J, Eckert C (2005) Is negative selection appropriate for anomaly detection? In: Proceedings of genetic and evolutionary computation conference (GECCO) 2005, pp 321–328Google Scholar
  33. 33.
    Matzinger P (1994) Tolerance, danger, and the extended family. Annu Rev Immunol 12(1):991–1045Google Scholar
  34. 34.
    Medzhitov R, Janeway CA Jr. (2002) Decoding the patterns of self and nonself by the innate immune system. Science 296(5566):298–300CrossRefGoogle Scholar
  35. 35.
    Greensmith J, Aickelin U, Cayzer S (2005) Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Proceedings of international conference on artificial immune systems (ICARIS) 2005, pp 153–167Google Scholar
  36. 36.
    Kim J, Bentley P, Wallenta C, Ahmed M, Hailes S (2006) Danger is ubiquitous: detecting malicious activities in sensor networks using the dendritic cell algorithm. In: Proceedings of international conference on artificial immune systems (ICARIS) 2006, pp 390–403Google Scholar
  37. 37.
    Greensmith J, Aickelin U (2007) Dendritic cells for SYN scan detection. In: Proceedings of genetic and evolutionary computation conference (GECCO) 2007, pp 49–56Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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