ICDMS: An Intelligent Cloud Based Disaster Management System for Vehicular Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7266)


The importance of emergency response systems cannot be overemphasized today due to the many manmade and natural disasters in the recent years such as September 2001 and the recent Japan earthquake and tsunami disaster. The overall cost of the Japan disaster alone is estimated to have exceeded 300 billion USD. Transportation and telecommunications play a critical role in disaster response and management in order to minimize loss of human life, economic cost and disruptions. Our research is concerned with developing emergency response systems for disasters of various scales with a focus on transportation systems, which exploit ICT developments.

In this paper, we leverage Intelligent Transportation Systems including Vehicular Ad hoc Networks, mobile and Cloud Computing technologies to propose an intelligent disaster management system. The system is intelligent because it is able to gather information from multiple sources and locations, including from the point of incident, and is able to let vehicles make effective strategies and decisions of communication protocols usage. Hybrid vehicular communications based on vehicle-to-vehicle and vehicle-to-infrastructure protocols are opportunistically exploited. The effectiveness of our system is demonstrated through modelling the impact of a disaster on a real city transport environment and comparing it with the case where our disaster management system was in place. We report great benefits derived from the adoption of our proposed system in terms of improved and balanced traffic flow and smooth evacuation.


Disaster Management and Resilience Intelligent Transportation Systems Cloud Computing VANETs Mobile Communications 


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  1. 1.
    Vásconez, K.C., Kehrli, M.: Highway Evacuations in Selected Metropolitan Areas: Assessment of Impediments. Techical Report: FHWA-HOP-10-059, Federal Highway Administration, Office of Transportation Operations, Washington, DC 20590, p. 107 (2010)Google Scholar
  2. 2.
    Owens, N., et al.: Traffic Incident Management Handbook. Science Applications International Corporation (SAIC): 8301 Greensboro Drive McLean, VA 22102, pp. 116 (2010)Google Scholar
  3. 3.
    RITA | ITS | Emergency Transportation Operations (June 27, 2011), (accessed: June 27, 2011)
  4. 4.
    Metropolitan Government of Nashville and Davidson County, Tennessee, Emergency Preparedness Survey for Davidson County (2008)Google Scholar
  5. 5.
    Drake, R.: The Hierarchy of Emergency Preparedness. In: Hakim, S., Blackstone, E.A. (eds.) Safeguarding Homeland Security, pp. 31–40. Springer, New York (2009)CrossRefGoogle Scholar
  6. 6.
    Departments of Transportation & Housing & Urban Development and Related Agencies Appropriations Act, 2010 Conference Report (111-366) to Accompany HR 3288 & Public Law 111-117, FY 2010 Consolidated Appropriations Act (2010)Google Scholar
  7. 7.
    Buchenscheit, A., Schaub, F., Kargl, F., Weber, M.: A VANET-based emergency vehicle warning system. 2009 IEEE Presented at the Vehicular Networking Conference (VNC), pp. 1–8 (2009)Google Scholar
  8. 8.
    Rizvi, S.R.A., Olariu, S., Rizvi, M.E., Weigle, M.C.: A Traffic Chaos Reduction Approach for Emergency Scenarios. In: IEEE International Performance, Computing, and Communications Conference, IPCCC 2007, pp. 576–578 (2007)Google Scholar
  9. 9.
    Furht, B., Escalante, A. (eds.): Handbook of Cloud Computing, 1st edn. SpringerGoogle Scholar
  10. 10.
    Alazawi, Z., Altowaijri, S., Mehmood, R., Abdljabar, M.B.: Intelligent disaster management system based on cloud-enabled vehicular networks. In: Proc. of 11th International Conference on ITS Telecommunications (ITST), August 23-25, pp. 361–368 (2011)Google Scholar
  11. 11.
    Chee, B.J.S., Curtis Franklin, J.: Cloud Computing: Technologies and Strategies of the Ubiquitous Data Center. CRC Press, Inc. (2010)Google Scholar
  12. 12.
    Murray: Enterprise grade cloud computing. In: Proceedings of the Third Workshop on Dependable Distributed Data Management, Nuremberg, Germany, p. 1 (2009)Google Scholar
  13. 13.
    Leimeister, S., Riedl, C., Krcmar, H., Böhm, M.: Cloud Computing - Outsourcing 2.0 or a new Business Model for IT Provisioning?, pp. 2–26Google Scholar
  14. 14.
    Youseff, L., Butrico, M., Da Silva, D.: Toward a Unified Ontology of Cloud Computing. In: Grid Computing Environments Workshop, GCE 2008, pp. 1–10 (2008)Google Scholar
  15. 15.
    Schweiger, B., Ehnert, P., Schlichter, J.: Simulative Evaluation of the Potential of Car2X-Communication in Terms of Efficiency. In: Strang, T., Festag, A., Vinel, A., Mehmood, R., Rico Garcia, C., Röckl, M. (eds.) Nets4Trains/Nets4Cars 2011. LNCS, vol. 6596, pp. 155–164. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Mehmood, R., Nekovee, M.: Vehicular Ad hoc and Grid Networks: Discussion, Design and Evaluation. In: Proc. of the 14th World Congress on Intelligent Transport Systems, p. 8 (2007)Google Scholar
  17. 17.
    Mehmood, R., Crowcroft, J., Hand, S., Smith, S.: Grid-Level Computing Needs Pervasive Debugging. In: The 6th IEEE/ACM International Workshop on Grid Computing, Seattle, WA, USA, pp. 186–193 (2005)Google Scholar
  18. 18.
    Rao, S.V.R.K., Diwanji, V.: WiMax’ble Pervasive Cloud – Empowering Next Generation Intelligent Railway Infrastructure. In: Strang, T., Festag, A., Vinel, A., Mehmood, R., Rico Garcia, C., Röckl, M. (eds.) Nets4Trains/Nets4Cars 2011. LNCS, vol. 6596, pp. 58–68. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Master Plan of the City of Al-Ramadi, Second Stage Report: Analysis of Existing Situation, Regional Context and Major Development Issues. Hydrosult Center for Engineering Planning (HCEP), Montreal, Canada (November 2009)Google Scholar
  20. 20.
    Mehmood, R.: Disk-based techniques for efficient solution of large markov chains. School of Computer Science, University of Birmingham, UK (2004)Google Scholar
  21. 21.
    Mehmood, R., Crowcroft, J.: Parallel Iterative Solution Method for Large Sparse Linear Equation Systems. Computer Laboratory: University of Cambridge (2005)Google Scholar
  22. 22.
    Mehmood, R.: Towards Understanding Intercity Traffic Interdependencies. In: Proc. of the 14th World Congress on Intelligent Transport Systems (2007)Google Scholar
  23. 23.
    Ayres, G., Mehmood, R.: On Discovering Road Traffic Information Using Virtual Reality Simulations. In: International Conference on Computer Modeling and Simulation, Los Alamitos, CA, USA, pp. 411–416 (2009)Google Scholar
  24. 24.
    Vegni, A.M., Little, T.D.C.: A Message Propagation Model for Hybrid Vehicular Communication Protocols. In: Proc. of IEEE 2nd Intl. Workshop on Communication Technologies for Vehicles (Nets4Cars 2010), Newcastle, UK, July 21-23 (2010)Google Scholar
  25. 25.
    Mejri, N., Kamoun, F., Filali, F.: Cooperative infrastructure discovery through V2X communication. In: Proc. of the 9th IFIP Annual Mediterranean Ad Hoc Networking Workshop, pp. 1–8 (June 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of EngineeringSwanseaUK
  2. 2.Al-Mustansereya UniversityBaghdadIraq
  3. 3.University of Roma TreRomeItaly

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