A Decentralized Trust Model to Reduce Information Unreliability in Complex Disaster Relief Operations

  • Dionysios Kostoulas
  • Roberto Aldunate
  • Feniosky Peña-Mora
  • Sanyogita Lakhera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4200)


The vulnerability of urban areas to extreme events is a vital challenge confronting society today. Response to such events involves a large number of organizations that had no past interactions with each other but are required to collaborate during disaster relief efforts. Participants from diverse teams need to form an integrated first response group to effectively react to extreme situations. Civil engineers are expected to play a key role in collaborative first response groups, because of their structural expertise, as complex disasters in urban areas are usually followed by structural damage of critical physical infrastructure. The establishment of trust is a major challenge in extreme situations that involve diverse response teams. Although the means of communication are available (e.g., ad-hoc networks), first responders are hesitant to interact with others outside of their organization because of no prior experience of interactions with them. Moreover, the spread of inaccurate information in cases of complex disaster relief operations increases uncertainty and risk. Participants must be given the ability to assess the trustworthiness of others and information propagated by them in order to enforce collaboration. In this paper, we propose a decentralized trust model to reduce uncertainty and support reliable information dissemination in complex disaster relief scenarios. Our model includes a distributed recommendation scheme, incorporated into an existing membership maintenance service for ad-hoc networks, and a nature-inspired activation spreading mechanism that allows trust-based information propagation. To evaluate the effectiveness of our method in reducing information unreliability in complex disaster areas, we tested it through software simulations and by conducting a search and rescue exercise involving civil engineers and firefighters. Results indicate fast and robust establishment of trust and high resilience to the spread of unreliable information.


Trust Model Structural Engineer Trust Rating Trust Relationship Reputation System 
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.
    New York Times: The 9/11 Report (9/11/2001), Web Page:
  2. 2.
    Newsweek: Tsunami Report (1/4/05), Web Page:
  3. 3.
    Mileti, D.: Disasters by Design: A Reassessment of Natural Hazards in United States. Joseph Henry Press, Washington (1999)Google Scholar
  4. 4.
    Tierney, K., Perry, R., Lindell, M.: Facing the Unexpected: Disaster Preparedness and Response in the United States. The National Academies Press, WashingtonGoogle Scholar
  5. 5.
    Columbia/Wharton Roundtable: Risk Management Strategies in an Uncertain World. In: IBM Palisades Executive Conference Center (April 2002)Google Scholar
  6. 6.
    Godschalk, D.: Urban Hazard Mitigation: Creating Resilient Cities. Natural Hazards Review, ASCE, 136–146 (August 2003)Google Scholar
  7. 7.
    Prieto, R.: The 3Rs: Lessons Learned from September 11th. Royal Academy of Engineering, Chairman Emeritus of Parsons Brinckerhoff, Co-chair, New York City Partnership Infrastructure Task Force (October 2002)Google Scholar
  8. 8.
    National Science and Technology Council: Committee on the Environment and Natural Resources. Reducing Disaster Vulnerability through Science and Technology (July 2003)Google Scholar
  9. 9.
    Quarantelli, E.L.: Major Criteria for Judging Disaster Planning and Managing and Their Applicability in Developing Societies. Disaster Research Center, University of Delaware, Newark, DelawareGoogle Scholar
  10. 10.
    Comfort, L.: Coordination in Complex Systems: Increasing Efficiency in Disaster Mitigation and Response. Annual Meeting of the American Political Science Association, San Francisco, USA (2001)Google Scholar
  11. 11.
    FEMA: Federal Response Plan Basic Plan (October 22, 2004),
  12. 12.
    der Heide, E.A.: Disaster Response – Principles of Preparation and Coordination. St. Louis, Mosby (1989)Google Scholar
  13. 13.
    FEMA: Federal Response Plan. Federal Emergency Management Agency, 9130.1-PL (April 1999)Google Scholar
  14. 14.
    Shuster, P.: The Disaster of Central Control. Complexity 9(4) (March-April 2004)Google Scholar
  15. 15.
    MSCMC, NCSA, Multi-Sector Crisis Management Consortium (2003), Web page:
  16. 16.
    Lee, R., Murphy, J.: PSWN Program Continues to Provide Direct Assistance to States Working to Improve Public Safety Communications. Homeland Defense Journal 1(22) (December 2002)Google Scholar
  17. 17.
    Leonardi, L., Mamei, M., Zambonelli, F.: Co-Fields: A Unifying Approach to Swarm Intelligence. In: 3rd International Workshop on Engineering Societies in the Agents World, Madrid (September 2002)Google Scholar
  18. 18.
    D’ Silva, S.: Collective Decision Making in Honey Bees: Selection of Nectar Sources and Distribution of Nectar Foragers Through Self-organization. Spring 1998 Colloquium, Michigan State University, Department of Physics & Astronomy (March 1998)Google Scholar
  19. 19.
    Agassounon, W.: Distributed information retrieval and dissemination in swarm-based networks of mobile autonomous agents. In: IEEE Swarm Intelligence Symposium, Indianapolis, USA (April 2003)Google Scholar
  20. 20.
    Yingying, D., Yan, H., Jingping, J.: Multi-robot cooperation method based on the ant algorithm. In: IEEE Swarm Intelligence Symposium, Indianapolis, USA (April 2003)Google Scholar
  21. 21.
    Watts, D.J.: A simple model of fads and cascading Failures, Working Papers 00-12-062, Santa Fe Institute (December 2000)Google Scholar
  22. 22.
    Hung, Y.T., Dennis, A., Robert, L.: Trust in Virtual Teams: Towards an Integrative Model of Trust Formation. In: Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS 2004), Big Island, Hawaii, January 5-8 (2004)Google Scholar
  23. 23.
    Marti, S., Giuli, T., Lai, K., Baker, M.: Mitigating routing misbehavior in mobile ad hoc networks. In: Proceedings of The Sixth International Conference on Mobile Computing and Networking 2000, Boston, MA (August 2000)Google Scholar
  24. 24.
    Liu, Z., Joy, A.W., Thompson, R.A.: A dynamic trust model for mobile ad-hoc networks. In: 10th IEEE International Workshop on Future Trends of Distributed Computing Systems (FTDCS 2004), pp. 80–85 (2004)Google Scholar
  25. 25.
    Buchegger, S., Boudec, J.-Y.: Performance analysis of the confidant protocol: Cooperation of nodes. In: IEEE/ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Lausanne (June 2002)Google Scholar
  26. 26.
    Buchegger, S., Le Boudec, J.-Y.: A Robust Reputation System for P2P and Mobile Ad-hoc Networks. In: 2nd Workshop on the Economics of Peer-to-Peer Systems, Harvard, June 4-5 (2004)Google Scholar
  27. 27.
    Abdul-Rahman, A., Hailes, S.: A distributed trust model. ACM New Security (1997)Google Scholar
  28. 28.
    Friedman, R., Tcharny, G.: Evaluating Failure Detection in Mobile Ad-Hoc Networks, Technical Report, Technion, Israel Institute of Technology, CS-2003-06Google Scholar
  29. 29.
    Aldunate, R., Ochoa, S.F., Pena Mora, F., Nussbaum, M.: Robot Mobile Ad-hoc Space for Collaboration to Support Disaster Relief Efforts Involving Critical Physical Infrastructure. J. Comp. in Civ. Engrg. 20(1), 13–27 (2006)CrossRefGoogle Scholar
  30. 30.
    Denning, P.J.: Hastily Formed Networks. Communications of the ACM 49(4) (April 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dionysios Kostoulas
    • 1
  • Roberto Aldunate
    • 2
  • Feniosky Peña-Mora
    • 3
  • Sanyogita Lakhera
    • 4
  1. 1.Graduate Student, Department of Computer Science, Newmark Civil Engineering LaboratoryUniversity of IllinoisUrbana-Champaign
  2. 2.PhD Student, Construction Management and Information Technology Group, Department of Civil and Environmental Engineering, Newmark Civil Engineering LaboratoryUniversity of IllinoisUrbana-Champaign
  3. 3.Associate Professor of Construction Management and Information Technology, Department of Civil and Environmental Engineering, Newmark Civil Engineering LaboratoryUniversity of IllinoisUrbana-Champaign
  4. 4.Graduate Student, Information Technology Group, Department of Civil and Environmental Engineering, Newmark Civil Engineering LaboratoryUniversity of IllinoisUrbana-Champaign

Personalised recommendations