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Design and Simulation of a Low-Resource Processing Platform for Mobile Multi-agent Systems in Distributed Heterogeneous Networks

  • Stefan Bosse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)

Abstract

The design and simulation of an agent processing platform suitable for distributed computing in heterogeneous sensor networks consisting of low-resource nodes is presented, providing a unique distributed programming model and enhanced robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures. In this work multi-agent systems with mobile activity-based agents are used for sensor data processing in unreliable mesh-like networks of nodes, consisting of a single microchip with limited low computational resources, which can be integrated into materials and technical structures. The agent behaviour, based on an activity-transition graph model, the interaction, and mobility can be efficiently integrated on the microchip using a configurable pipelined multi-process architecture based on the Petri-Net model and token-based processing. A new sub-state partitioning of activities simplifies and optimizes the processing platform significantly. Additionally, software implementations and simulation models with equal functional behaviour can be derived from the same program source. Hardware, software, and simulation platforms can be directly connected in heterogeneous networks. Agent interaction and communication is provided by a simple tuple-space database. A reconfiguration mechanism of the agent processing system offers activity graph changes at run-time. The suitability of the agent processing platform in large scale networks is demonstrated by using agent-based simulation of the platform architecture at process level with hundreds of nodes.

Keywords

Multi-agent platform Sensor network Mobile agent Heterogeneous networks Embedded systems 

References

  1. 1.
    Bosse, S.: Distributed agent-based computing in material-embedded sensor network systems with the agent-on-chip architecture. IEEE Sens. J., Special Issue MIS 14, 2159–2170 (2014). doi: 10.1109/JSEN.2014.2301938 CrossRefGoogle Scholar
  2. 2.
    Guijarro, M., Fuentes-Fernandez, R., Pajares, G.: A Multi-Agent System Architecture for Sensor Networks, Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications (2008)Google Scholar
  3. 3.
    Zhao, X., Yuan, S., Yu, Z., Ye, W., Cao, J.: Designing strategy for multi-agent system based large structural health monitoring. Expert Syst. Appl. 34(2), 1154–1168 (2008). doi: 10.1016/j.eswa.2006.12.022 CrossRefGoogle Scholar
  4. 4.
    Pantke, F., Bosse, S., Lehmhus, D., Lawo, M.: An artificial intelligence approach towards sensorial materials. In: Future Computing Conference (2011)Google Scholar
  5. 5.
    Klügel, F.: SeSAm: visual programming and participatory simulation for agent-based models. In: Uhrmacher, A.M., Weyns, D. (eds.) Multi-agent Systems - Simulation and Applications. CRC Press, Boca Raton (2009)Google Scholar
  6. 6.
    Bosse, S.: Hardware-software-co-design of parallel and distributed systems using a unique behavioural programming and multi-process model with high-level synthesis In: Proceedings of the SPIE Microtechnologies 2011 Conference, Session EMT 102. doi: 10.1117/12.888122
  7. 7.
    Kone, M.T., Shimazu, A., Nakajima, T.: The state of the art in agent communication languages. Knowl. Inf. Syst. 2(3), 259–284 (2000). doi: 10.1007/PL00013712 CrossRefzbMATHGoogle Scholar
  8. 8.
    Ebrahimi, M., Daneshtalab, M., Liljeberg, P., Plosila, J., Tenhunen, H.: Agent-based on-chip network using efficient selection method. In: 2011 IEEEIFIP 19th International Conference on VLSI and System-on-Chip, pp. 284–289 (2011)Google Scholar
  9. 9.
    Sansores, C., Pavon, J.: An adaptive agent model for self-organizing MAS. In: Proceedings of 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, 12–16 May 2008, pp. 1639–1642 (2008)Google Scholar
  10. 10.
    McCabe, F.G., Clark, K.L.: APRIL — agent process interaction language. In: Wooldridge, M., Jennings, N.R. (eds.) ECAI 1994. LNCS, vol. 890, pp. 324–340. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  11. 11.
    Lang, W., Jakobs, F., Tolstosheeva, E., Sturm, H., Ibragimov, A., Kesel, A., Lehmhus, D., Dicke, U.: From embedded sensors to sensorial materials–the road to function scale integration. Sens. Actuators A Phys. 171(1), 3–11 (2011)CrossRefGoogle Scholar
  12. 12.
    Liu, J.: Autonomous Agents and Multi-agent Systems. World Scientific Publishing, River Edge (2001). ISBN 981-02-4282-4CrossRefGoogle Scholar
  13. 13.
    Meng, Y.: An agent-based reconfigurable system-on-chip architecture for real-time systems. In: Proceedings of the Second International Conference on Embedded Software and Systems (ICESS 2005), pp. 166–173 (2005)Google Scholar
  14. 14.
    Jamont, J.-P., Occello, M.: A multiagent method to design hardware/software collaborative systems. In: 12th International Conference on Computer Supported Cooperative Work in Design (2008)Google Scholar
  15. 15.
    Naji, H.: Creating an adaptive embedded system by applying multi-agent techniques to reconfigurable hardware. Future Gener. Comput. Syst. 20(6), 1055–1081 (2004)CrossRefGoogle Scholar
  16. 16.
    Bosse, S.: Design of material-integrated distributed data processing platforms with mobile multi-agent systems in heterogeneous networks. In: Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART 2014) (2014). doi: 10.5220/0004817500690080

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics & Computer Science, ISIS Sensorial Materials Scientific CentreUniversity of BremenBremenGermany

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