An Emergency Response System for Intelligent Buildings

  • Avgoustinos Filippoupolitis
  • Erol Gelenbe
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 12)


Finding the best evacuation path during an emergency situation inside a building is a challenging task, due to the dynamically changing conditions and the strict time constraints. Information systems can benefit the evacuation process by providing directions to the evacuees in an efficient and timely manner. In this paper we propose the use of such a system and evaluate it with a specialised software platform that we have developed for simulation of disasters in buildings. The system provides movement decision support to evacuees by directing them through the less hazardous routes to an exit. It is composed of a network of Decision Nodes and sensor nodes, positioned in specific locations inside the building. The recommendations of the Decision Nodes are computed in a distributed manner, at each of the Decision Nodes, which then communicate them to evacuees or rescue personnel located in their vicinity. The system computes the best evacuation routes in real-time, while a hazard is spreading inside the building. It also takes into account the spatial characteristics of hazard propagation inside a confined space. Our simulation results show that the outcome of the evacuation procedure is improved by the use of the decision support system.


Sensor Network Sensor Node Decision Support System Decision Node Hazardous Area 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Batalin, M., Sukhatme, G.S., Hattig, M.: Mobile robot navigation using a sensor network. In: IEEE International Conference on Robotics and Automation, New Orleans, Louisiana, pp. 636–642 (2004)Google Scholar
  2. 2.
    Bertsekas, D., Gallager, R.: Data networks. Prentice-Hall, Inc., Upper Saddle River (1987)Google Scholar
  3. 3.
    Corke, P., Peterson, R., Rus, D.: Networked robots: Flying robot navigation using a sensor net. In: 11th International Symposium of Robotics Research (ISRR 2003), Siena, Italy, pp. 234–243 (2003)Google Scholar
  4. 4.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. The MIT Press (2001)Google Scholar
  5. 5.
    Dimakis, N., Filippoupolitis, A., Gelenbe, E.: Distributed building evacuation simulator for smart emergency management. The Computer Journal 53(9), 1384–1400 (2010)CrossRefGoogle Scholar
  6. 6.
    Elms, D.G., Buchanan, A.H., Dusing, J.W.: Modeling fire spread in buildings. Fire Technology 20(1), 11–19 (1984)CrossRefGoogle Scholar
  7. 7.
    Filippoupolitis, A., Gelenbe, E.: A distributed decision support system for building evacuation. In: Proceedings of the 2nd IEEE International Conference on Human System Interaction, Catania, Italy, pp. 323–330 (2009)Google Scholar
  8. 8.
    Filippoupolitis, A., Hey, L., Loukas, G., Gelenbe, E., Timotheou, S.: Emergency response simulation using wireless sensor networks. In: Proceedings of the 1st International Conference on Ambient Media and Systems, Quebec City, Canada, pp. 1–8 (2008)Google Scholar
  9. 9.
    FIPA: The Foundation for Intelligent Physical Agents,
  10. 10.
    Gelenbe, E.: Sensible decisions based on QoS. Computational Management Science 1(1), 1–14 (2003)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Gelenbe, E., Lent, R., Xu, Z.: Measurement and performance of a cognitive packet network. Journal of Computer Networks 37(6), 691–701 (2001)CrossRefGoogle Scholar
  12. 12.
    Gelenbe, E., Seref, E., Xu, Z.: Simulation with Learning Agents. Proceedings of the IEEE 89(2), 148–157 (2001)CrossRefGoogle Scholar
  13. 13.
    Hasofer, A.M., Odigie, D.O.: Stochastic modelling for occupant safety in a building fire. Fire Safety Journal 36, 269–289 (2001)CrossRefGoogle Scholar
  14. 14.
    Humblet, P.A.: Another adaptive distributed shortest path algorithm. IEEE Transactions on Communications 39, 995–1003 (1991)zbMATHCrossRefGoogle Scholar
  15. 15.
    Li, Q., Rosa, M.D., Rus, D.: Distributed algorithms for guiding navigation across a sensor network. In: MobiCom 2003: Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, San Diego, CA, USA, pp. 313–325 (2003)Google Scholar
  16. 16.
    Tseng, Y.C., Pan, M.S., Tsai, Y.Y.: Wireless sensor networks for emergency navigation. Computer 39(7), 55–62 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Avgoustinos Filippoupolitis
    • 1
  • Erol Gelenbe
    • 1
  1. 1.Department of Electrical & Electronic EngineeringImperial CollegeLondonUK

Personalised recommendations