Bio-inspired Network-Centric Operation and Control for Sensor/Actuator Networks

  • Falko Dressler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4780)

Abstract

Self-organization mechanisms have been investigated and developed to efficiently operate networked embedded systems. Special focus was given to wireless sensor networks (WSN) and sensor/actuator networks (SANET). Looking at the most pressing issues in such networks, the limited resources and the huge amount of interoperating nodes, the proposed solutions primarily intend to solve the scalability problems by reducing the overhead in data communication. Well-known examples are data-centric routing approaches and probabilistic techniques. In this paper, we intend to go one step further. We are about to also move the operation and control for WSN and SANET into the network. Inspired by the operation of complex biological systems such as the cellular information exchange, we propose a network-centric approach. Our method is based on three concepts: data-centric operation, specific reaction on received data, and simple local behavior control using a policy-based state machine. In summary, these mechanisms lead to an emergent system behavior that allows to control the operation of even large-scale sensor/actuator networks.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Estrin, D., Culler, D., Pister, K., Sukhatme, G.S.: Connecting the Physical World with Pervasive Networks. IEEE Pervasive Computing 1, 59–69 (2002)CrossRefGoogle Scholar
  2. 2.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A Survey on Sensor Networks. IEEE Communications Magazine 40, 102–116 (2002)CrossRefGoogle Scholar
  3. 3.
    Akyildiz, I.F., Kasimoglu, I.H.: Wireless Sensor and Actor Networks: Research Challenges. Elsevier Ad Hoc Network Journal 2, 351–367 (2004)CrossRefGoogle Scholar
  4. 4.
    Margi, C.: A Survey on Networking, Sensor Processing and System Aspects of Sensor Networks. Report, University of California, Santa Cruz (2003)Google Scholar
  5. 5.
    Culler, D., Estrin, D., Srivastava, M.B.: Overview of Sensor Networks. Computer 37, 41–49 (2004)CrossRefGoogle Scholar
  6. 6.
    Chong, C.-Y., Kumar, S.P.: Sensor Networks: Evolution, Opportunities, and Challenges. Proceedings of the IEEE 91, 1247–1256 (2003)CrossRefGoogle Scholar
  7. 7.
    Parker, L.E.: Current Research in Multi-Robot System. Journal of Artificial Life and Robotics 7 (2004)Google Scholar
  8. 8.
    Kumar, V., Rus, D., Singh, S.: Robot and Sensor Networks for First Responders. IEEE Pervasive Computing 3, 24–33 (2004)CrossRefGoogle Scholar
  9. 9.
    Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., Anderson, J.: Wireless Sensor Networks for Habitat Monitoring. In: First ACM Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, USA, ACM Press, New York (2002)Google Scholar
  10. 10.
    Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: A scalable and robust communication paradigm for sensor networks. In: 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCOM’00), Boston, MA, USA, pp. 56–67. IEEE Computer Society Press, Los Alamitos (2000)CrossRefGoogle Scholar
  11. 11.
    Batalin, M.A., Sukhatme, G.S.: Using a Sensor Network for Distributed Multi-Robot Task Allocation. In: IEEE International Conference on Robotics and Automation, New Orleans, LA, USA, May 2003, pp. 158–164. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  12. 12.
    Muscettola, N., Nayak, P.P., Pell, B., Williams, B.C.: Remote Agent: To Boldly Go Where No AI System Has Gone Before. Artificial Intelligence 100, 5–48 (1998)CrossRefGoogle Scholar
  13. 13.
    Eigen, M., Schuster, P.: The Hypercycle: A Principle of Natural Self Organization. Springer, Berlin (1979)Google Scholar
  14. 14.
    Dressler, F.: Efficient and Scalable Communication in Autonomous Networking using Bio-inspired Mechanisms - An Overview. Informatica - An International Journal of Computing and Informatics 29, 183–188 (2005)Google Scholar
  15. 15.
    Alberts, B., Bray, D., Lewis, J., Raff, M., Roberts, K., Watson, J.D.: Molecular Biology of the Cell, 3rd edn. Garland Publishing, Inc. (1994)Google Scholar
  16. 16.
    Pawson, T.: Protein modules and signalling networks. Nature 373, 573–580 (1995)CrossRefGoogle Scholar
  17. 17.
    Dressler, F., Krüger, B.: Cell biology as a key to computer networking. In: German Conference on Bioinformatics 2004 (GCB’04), Poster Session, Bielefeld, Germany (2004)Google Scholar
  18. 18.
    Krüger, B., Dressler, F.: Molecular Processes as a Basis for Autonomous Networking. IPSI Transactions on Advances Research: Issues in Computer Science and Engineering 1, 43–50 (2005)Google Scholar
  19. 19.
    Guyton, A.: Blood pressure control - special role of the kidneys and body fluids. Science 252, 1813–1816 (1991)CrossRefGoogle Scholar
  20. 20.
    Prehofer, C., Bettstetter, C.: Self-Organization in Communication Networks: Principles and Design Paradigms. IEEE Communications Magazine 43, 78–85 (2005)CrossRefGoogle Scholar
  21. 21.
    Jeong, J., Culler, D.: Incremental Network Programming for Wireless Sensors. In: First IEEE International Conference on Sensor and Ad hoc Communications and Networks (IEEE SECON), IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  22. 22.
    Fuchs, G., Truchat, S., Dressler, F.: Distributed Software Management in Sensor Networks using Profiling Techniques. In: 1st IEEE/ACM International Conference on Communication System Software and Middleware (IEEE COMSWARE 2006): 1st International Workshop on Software for Sensor Networks (SensorWare 2006), New Dehli, India, ACM Press, New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Falko Dressler
    • 1
  1. 1.Autonomic Networking Group, Dept. of Computer Science 7, University of Erlangen-NurembergGermany

Personalised recommendations