Abstract
The Natural Immune System (NIS) is a distributed system that solves challenging search and response problems while operating under constraints imposed by physical space and resource availability. Remarkably, NIS search and response times do not scale appreciably with the physical size of the animal in which its search is conducted. Many distributed systems are engineered to solve analogous problems, and the NIS demonstrates how such engineered systems can achieve desirable scalability. We hypothesize that the architecture of the NIS, composed of a hierarchical decentralized detection network of lymph nodes (LN) facilitates efficient search and response. A sub-modular architecture in which LN numbers and size both scale with organism size is shown to efficiently balance tradeoffs between local antigen detection and global antibody production, leading to nearly scale-invariant detection and response. We characterize the tradeoffs as balancing local and global communication and show that similar tradeoffs exist in distributed systems like LN inspired artificial immune system (AIS) applications and peer-to-peer (P2P) systems. Taking inspiration from the architecture of the NIS, we propose a modular RADAR (Robust Adaptive Decentralized search with Automated Response) strategy for distributed systems. We demonstrate how two existing distributed systems (a LN inspired multi-robot control application and a P2P system) can be improved by a modular RADAR strategy. Such a sub-modular architecture is shown to balance the tradeoffs between local communication (within artificial LNs and P2P clusters) and global communication (between artificial LNs and P2P clusters), leading to efficient search and response.
Chapter PDF
Similar content being viewed by others
Keywords
- scale invariant detection and response
- distributed systems
- scale invariant response
- scale invariant detection
- immune system scaling
- modular search
- modular architecture
- sub-modular architecture
- peer-to-peer systems
- artificial immune systems
- immune system modelling
- intrusion detection systems
- malware detection systems
- mobile ad-hoc networks
- disruption tolerant networks
- wireless sensor networks
- multi robot control
References
Kleinberg, J.: The Wireless Epidemic. Nature 449, 287–288 (2007)
Banerjee, S., Moses, M.: A Hybrid Agent Based and Differential Equation Model of Body Size Effects on Pathogen Replication and Immune System Response. In: Andrews, P.S. (ed.) ICARIS 2009. LNCS, vol. 5666, pp. 14–18. Springer, Heidelberg (2009)
Banerjee, S., Moses, M.: Scale Invariance of Immune System Response Rates and Times: Perspectives on Immune System Architecture. Swarm Intelligence (2010) (under review)
Peters, R.H.: The Ecological Implications of Body Size. Cambridge University Press, Cambridge (1983)
Soderberg, A.K., et al.: Innate Control of Adaptive Immunity via Remodeling of Lymph Node Feed Arteriole. PNAS 102, 16315–16320 (2005)
Huang, K.J., et al.: An interferon-gamma-related cytokine storm in SARS patients. Journal of Medical Virology 75(2), 185–194 (2005)
Surowiecki, J.: The wisdom of crowds. Little, Brown, London (2004)
Forrest, S., Beauchemin, C.: Computer immunology. Immunological Reviews 216, 176–197 (2007)
Diamond, M.S., et al.: A Critical Role for Induced IgM in the Protection against West Nile Virus Infection. Journal of Experimental Medicine (2003), doi:10.1084/jem20031223
Halin, C., et al.: In vivo imaging of lymphocyte trafficking. Ann. Rev. Cell Devel. Biol. 21, 581–603 (2005)
Altman, P.L., Dittmer, D.S.: Biology Data Book, 2nd edn., vol. 3. Federation of American Societies for Experimental Biology, Bethesda (1974)
Hildebrandt, T.B., et al.: Ultrasonographic assessment and ultra-sound guided biopsy of the retropharyngeal lymph nodes in Asian elephants (Elephas maximus). Vet. Rec. 157, 544–548 (2005)
Nair, S.B., et al.: An Immune System based Multi-Robot Mobile Agent Network. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 424–433. Springer, Heidelberg (2008)
Mokhtar, M., et al.: An Artificial Lymph Node Architecture for Homeostasis in Collective Robotic Systems. In: Workshop on Pervasive Adaptive Systems (2008)
Hart, E., Davoudani, D.: Dendritic Cell Trafficking, From Immunology to Engineering. In: Andrews, P.S. (ed.) ICARIS 2009. LNCS, vol. 5666, pp. 11–13. Springer, Heidelberg (2009)
Li, M., et al.: Semantic Small World: An Overlay Network for Peer-to-Peer Search. In: IEEE International Conference on Network Protocols (ICNP 2004), pp. 228–238 (2004)
Kleinberg, J.: The small-world phenomenon: An algorithmic perspective. In: Proc. 32nd ACM Symposium on Theory of Computing (2000)
Leskovec, J., et al.: Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations. In: Proc. 11th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (2005)
Lua, E.K., et al.: A Survey and Comparison of Peer-to-Peer Overlay Network Schemes. IEEE Communications Survey and Tutorial (2004)
Hofmeyr, S.A., Forrest, S.: Architecture for an artificial immune system. Evol. Comput. J. 8, 443–473 (2000)
Somayaji, A., Forrest, S.: Automated response using system-call delays. In: Usenix Security Symposium (2000)
Dasgupta, D.: Immunity-based intrusion detection system: A general framework. In: Proceedings of the 22nd National Information Systems Security Conference, NISSC (1999)
Delin, K.A.: The Sensor Web: a macro-instrument for coordinated sensing. Sensors 2, 270–285 (2002)
Brown, J.H.: Toward a Metabolic Theory of Ecology. Ecology 85, 1771–1789 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Banerjee, S., Moses, M. (2010). Modular RADAR: An Immune System Inspired Search and Response Strategy for Distributed Systems. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds) Artificial Immune Systems. ICARIS 2010. Lecture Notes in Computer Science, vol 6209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14547-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-14547-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14546-9
Online ISBN: 978-3-642-14547-6
eBook Packages: Computer ScienceComputer Science (R0)