Skip to main content

An Engineering-Informed Modelling Approach to AIS

  • Conference paper
Artificial Immune Systems (ICARIS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6825))

Included in the following conference series:

  • 1034 Accesses

Abstract

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.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Forrest, S., Beauchemin, C.: Computer Immunology. Immunol. Rev. 216(1), 176–197 (2007)

    Article  Google Scholar 

  2. Timmis, J.: Artificial immune systems: Today and tomorow. Natural Computing 6(1), 1–18 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  5. Hart, E., Timmis, J.: Application areas of AIS: The past, the present and the future. Applied Soft Computing 8(1), 191–201 (2008)

    Article  Google Scholar 

  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. Ismail, A., Timmis, J.: Aggregation of swarms for fault tolerance in swarm robotics. In: UK Workshop on Computational Intelligence (2009)

    Google Scholar 

  8. Cohen, I.: Real and artificial immune systems: computing the state of the body. Nature Reviews Immunology 07, 569–574 (2007)

    Article  Google Scholar 

  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. Owens, N.D., Timmis, J., Greensted, A., Tyrrell, A.: Elucidation of t cell signalling models. Journal of Theoretical Biology 262(3), 452–470 (2010)

    Article  MathSciNet  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. Greensmith, J., Aickelin, U., Tedesco, G.: Information fusion for anomaly detection with the dendritic cell algorithm. Information Fusion 11(1), 21–34 (2010)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  16. Randolph, G.: Dendritic-cell trafficking to lymph nodes through lymphatic vessels. Nature Reviews Immunology 5(8), 617–628 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Willensky, U.: Netlogo. Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston, IL (1999), http://ccl.northwestern.edu/netlogo

  19. Randolph, G.: Is maturation required for langerhans cell migration? J. Exp. Med. 196(4), 413–416 (2002)

    Article  Google Scholar 

  20. Janeway, C.A., Paul, T.: Immunobiology: The Immune System in Health and Disease, 3rd edn. Garland Publishing, New York (1997)

    Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  23. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hart, E., Davoudani, D. (2011). An Engineering-Informed Modelling Approach to AIS. In: Liò, P., Nicosia, G., Stibor, T. (eds) Artificial Immune Systems. ICARIS 2011. Lecture Notes in Computer Science, vol 6825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22371-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22371-6_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22370-9

  • Online ISBN: 978-3-642-22371-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics