An Engineering-Informed Modelling Approach to AIS

  • Emma Hart
  • Despina Davoudani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6825)


A recent shift in thinking in Artificial Immune Systems (AIS) advocates developing a greater understanding of the underlying biological systems that serve as inspiration for engineering such systems by developing abstract computational models of the immune system in order to better understand the natural biology. We propose a refinement to existing frameworks which requires development of such models to be driven by the engineering problem being considered; the constraints of the engineered system must inform not only the model development, but also its validation. Using a case-study, we present a methodology which enables an abstract model of dendritic-cell trafficking to be developed with the purpose of building a self-organising wireless sensor network for temperature monitoring and maintenance. The methodology enables the development of a model which is consistent with the application constraints from the outset and can be validated in terms of the functional requirements of the application. Although the result models are not likely to be biologically faithful, they enable the engineer to better exploit the underlying metaphor, ultimately leading to reduced development time of the engineered system.


Lymph Node Dendritic Cell Infected Site Anomaly Detection Engineering Constraint 
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.


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  1. 1.
    Forrest, S., Beauchemin, C.: Computer Immunology. Immunol. Rev. 216(1), 176–197 (2007)CrossRefGoogle Scholar
  2. 2.
    Timmis, J.: Artificial immune systems: Today and tomorow. Natural Computing 6(1), 1–18 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Lau, H., Bate, I., Timmis, J.: An immuno-engineering approach for anomaly detection in swarm robotics. In: Andrews, P.S., Timmis, J., Owens, N.D.L., Aickelin, U., Hart, E., Hone, A., Tyrrell, A.M. (eds.) ICARIS 2009. LNCS, vol. 5666, pp. 136–150. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Davoudani, D., Hart, E., Paechter, B.: Computing the state of specknets: Further analysis of an innate immune-inspired model. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 95–106. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Hart, E., Timmis, J.: Application areas of AIS: The past, the present and the future. Applied Soft Computing 8(1), 191–201 (2008)CrossRefGoogle Scholar
  6. 6.
    Arvind, D., Wong, K.: Speckled computing: Disruptive technology for networked information appliances. In: Proceedings of the IEEE International Symposium on Consumer Electronics (ISCE 2004), pp. 219–223 (2004)Google Scholar
  7. 7.
    Ismail, A., Timmis, J.: Aggregation of swarms for fault tolerance in swarm robotics. In: UK Workshop on Computational Intelligence (2009)Google Scholar
  8. 8.
    Cohen, I.: Real and artificial immune systems: computing the state of the body. Nature Reviews Immunology 07, 569–574 (2007)CrossRefGoogle Scholar
  9. 9.
    Stepney, S., Smith, R., Timmis, J., Tyrrell, A., Neal, M., Hone, A.: Conceptual frameworks for artificial immune systems. Int. J. Unconventional Computing 1(3), 315–338 (2006)Google Scholar
  10. 10.
    Owens, N.D., Timmis, J., Greensted, A., Tyrrell, A.: Elucidation of t cell signalling models. Journal of Theoretical Biology 262(3), 452–470 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Owens, N., Greensted, A., Timmis, J., Tyrrell, A.: T cell receptor signalling inspired kernel density estimation and anomaly detection. In: Andrews, P.S., Timmis, J., Owens, N.D.L., Aickelin, U., Hart, E., Hone, A., Tyrrell, A.M. (eds.) ICARIS 2009. LNCS, vol. 5666, pp. 122–135. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Andrews, P.: An Investigation of a Methodology for the Development of Artifical Immune Systenms: A Case Study in Immune Receptor Degeneracy. PhD thesis, University of York (2008)Google Scholar
  13. 13.
    Dilger, W., Strangfeld, S.: Properties of the Bersini experiment on self-assertion. In: Cattolico, M. (ed.) GECCO, pp. 95–102. ACM, New York (2006)Google Scholar
  14. 14.
    Greensmith, J., Aickelin, U., Tedesco, G.: Information fusion for anomaly detection with the dendritic cell algorithm. Information Fusion 11(1), 21–34 (2010)CrossRefGoogle Scholar
  15. 15.
    Timmis, J., Hart, E., Hone, A., Neal, M., Robins, A., Stepney, S., Tyrrell, A.: Immuno-engineering. In: Biologically-Inspired Collaborative Computing, vol. 268, pp. 3–17. Springer, Boston (2008)CrossRefGoogle Scholar
  16. 16.
    Randolph, G.: Dendritic-cell trafficking to lymph nodes through lymphatic vessels. Nature Reviews Immunology 5(8), 617–628 (2005)CrossRefGoogle Scholar
  17. 17.
    Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. PNAS: Proceedings of the National Academemy of Sciences of the United States of America 99(suppl.3), 7280–7287 (2002)CrossRefGoogle Scholar
  18. 18.
    Willensky, U.: Netlogo. Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston, IL (1999),
  19. 19.
    Randolph, G.: Is maturation required for langerhans cell migration? J. Exp. Med. 196(4), 413–416 (2002)CrossRefGoogle Scholar
  20. 20.
    Janeway, C.A., Paul, T.: Immunobiology: The Immune System in Health and Disease, 3rd edn. Garland Publishing, New York (1997)Google Scholar
  21. 21.
    Ye, N.F., Chen, F.Y.A.: A scalable solution to minimum cost forwarding in large sensor. In: Computer Communications and Networks, pp. 304–309 (2001)Google Scholar
  22. 22.
    Faruque, J., Psounis, K., Helmy, A.: Analysis of gradient-based routing protocols in sensor networks. In: Distributed Computing in Sensor Systems: First IEEE International Conference, pp. 258–275. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Emma Hart
    • 1
  • Despina Davoudani
    • 1
  1. 1.Edinburgh Napier UniversityEdinburghScotland, UK

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