Time Analysis of the Integration of Simulators for an AmI Environment

  • Álvaro Sánchez-PicotEmail author
  • Diego Sánchez-de-Rivera
  • Tomás Robles
  • Jaime Jiménez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)


There is a trend nowadays where each time there are more and more devices around us and all of them need to be connected between them and to the Internet so that the data they provide or the service they offer can be accessed anywhere anytime and the can coordinate between them to achieve a greater goal. Aside from the devices, the other important agents in this environment are the people that move around while the devices monitor their activities. With all this complex environment in mind it becomes clear that the use of simulators to improve it is necessary. In this paper we analyze the communication necessary in our AmI environment simulator composed of an engine, an existing social simulator and an existing network simulator. We also propose a mathematical model for the times of the different messages sent between the simulators.


Simulation Social simulation Network simulation Ambient intelligence 



These results were supported by UPM’s “Programa Propio”, the Autonomous Region of Madrid through program MOSI-AGIL-CM (grant P2013/ICE-3019, co-funded by EU Structural Funds FSE and FEDER) and has also received funding from the Ministry of Economy and Competitiveness through SEMOLA project (TEC2015-68284- R).


  1. 1.
    Allan, R.J.: Survey of agent based modelling and simulation tools. Science and Technology Facilities Council, ISSN 1362-0207 (2010)Google Scholar
  2. 2.
    Breslau, L., Estrin, D., Fall, K., Floyd, S., Heidemann, J., Helmy, A., Yu, H.: Advances in network simulation. Computer 33(5), 59–67 (2000)CrossRefGoogle Scholar
  3. 3.
    Cabitza, F., Fogli, D., Lanzilotti, R., Piccinno, A.: Rule-based tools for the configuration of ambient intelligence systems: a comparative user study. Multimedia Tools Appl. 76(4), 5221–5241 (2017)CrossRefGoogle Scholar
  4. 4.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5(4), 277–298 (2009)CrossRefGoogle Scholar
  5. 5.
    Macal, C., North, M.: Introductory tutorial: agent-based modeling and simulation. In: Proceedings of the 2014 Winter Simulation Conference, pp. 6–20. IEEE Press, December 2014Google Scholar
  6. 6.
    Sánchez-Picot, Á., de Andrés, D.M., Sánchez, B.B., Garrido, R.P.A., de Rivera, D.S., Robles, T.: Towards a simulation of AmI environments integrating social and network simulations. In: AmILP@ ECAI (2016)Google Scholar
  7. 7.
    Sokolowski, J.A., Banks, C.M. (eds.): Principles of Modeling and Simulation: A Multidisciplinary Approach. Wiley, New York (2011)Google Scholar
  8. 8.
    Yuan, X., Cai, Z.P., Liu, S.H., Yu, Y.: Large-scale network emulation software and its key technologies. Comput. Technol. Dev. 7, 003 (2014)Google Scholar
  9. 9.
    Bordel Sánchez, B., Alcarria, R., Martín, D., Robles, T.: TF4SM: a framework for developing traceability solutions in small manufacturing companies. Sensors 15(11), 29478–29510 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Álvaro Sánchez-Picot
    • 1
    Email author
  • Diego Sánchez-de-Rivera
    • 1
  • Tomás Robles
    • 1
  • Jaime Jiménez
    • 2
  1. 1.Department of Telematics Systems EngineeringUniversidad Politécnica de MadridMadridSpain
  2. 2.EricssonKirkkonummiFinland

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