DrillSim: A Simulation Framework for Emergency Response Drills

  • Vidhya Balasubramanian
  • Daniel Massaguer
  • Sharad Mehrotra
  • Nalini Venkatasubramanian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)


Responding to natural or man-made disasters in a timely and effective manner can reduce deaths and injuries, contain or prevent secondary disasters, and reduce the resulting economic losses and social disruption. Appropriate IT solutions can improve this response. However, exhaustive and realistic validation of these IT solutions is difficult; proofs are not available, simulations lack realism, and drills are expensive and cannot be reproduced. This paper presents DrillSim: a simulation environment that plays out the activities of a crisis response (e.g., evacuation). It has capabilities to integrate real-life drills into a simulated response activity using an instrumented environment with sensing and communication capabilities. IT solutions can be plugged in the simulation system to study their effectiveness in disaster management and response. This way, by using a simulation coupled with an on-going drill, IT solutions can be tested in a less expensive but realistic scenario.


Emergency Response Simulation Framework Disaster Response Simulation Engine Smart Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    MicroOptical-SV-6 PC Viewer specification (2003)Google Scholar
  2. 2.
  3. 3.
    Ekahau Positioning Engine. (2005),
  4. 4.
    Interdependent Infrastructure Modeling, Simulation, and Analysis Project (SOFIA), Los Alamos National Laboratory (2005),
  5. 5.
  6. 6.
    Responsphere (2005),
  7. 7.
    Robocup-Rescue Simulation Project (2005),
  8. 8.
    Simulex: Simulation of Occupant Evacuation (2005),
  9. 9.
    The Urban Security Initiative, Los Alamos National Laboratory (2005),
  10. 10.
    Bahl, P., Padmanabhan, V.N.: RADAR: an In-Building RF-Based User Location and Tracking System. In: INFOCOM, vol. (2), pp. 775–784 (2000)Google Scholar
  11. 11.
    Bellifemine, F., Poggi, A., Rimassa, G., Turci, P.: An object oriented framework to realize agent systems. In: WOA 2000 (May 2000)Google Scholar
  12. 12.
    Cristoforetti, J.: Multimodal Systems in the Management of Emergency Situations. Master’s thesis, Universita de Bologna (2005)Google Scholar
  13. 13.
    Haykin, S.: Neural Networks - A Comprehensive Foundation. Prentice Hall, Englewood Cliffs (1999)MATHGoogle Scholar
  14. 14.
    Jain, S., McLean, C.R.: A Framework for Modeling and Simulation for Emergency Response. In: Winter Simulation Conference (2003)Google Scholar
  15. 15.
    Jain, S., McLean, C.R.: An Integrating Framework for Modeling and Simulation of Emergency Response. Simulation Journal: Transactions of the Society for Modeling and Simulation International (2003)Google Scholar
  16. 16.
    Jain, S., McLean, C.R.: An Architecture for Modeling and Simulation of Emergency Response. In: Proceedings of the 2004 IIE Conference (2004)Google Scholar
  17. 17.
    John, L., Hennessy, D.A.P., Lin, S.H.:Google Scholar
  18. 18.
    Mehrotra, S., Butts, C., Kalashnikov, D., Venkatasubramanian, N., Rao, R., Chockalingam, G., Eguchi, R., Adams, B., Huyck, C.: Project rescue: Challenges in responding to the unexpected. SPIE Journal of Electronic Imaging, Displays, and Medical Imaging 5304, 179–192 (2004)Google Scholar
  19. 19.
    Murakami, Y., Minami, K., et al.: Multi-Agent Simulation for Crisis Management. In: KMN (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vidhya Balasubramanian
    • 1
  • Daniel Massaguer
    • 1
  • Sharad Mehrotra
    • 1
  • Nalini Venkatasubramanian
    • 1
  1. 1.Donald Bren School of Information and Computer ScienceUniversity of California, IrvineIrvineUSA

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