Advertisement

Modeling Mobility with Behavioral Rules: The Case of Incident and Emergency Situations

  • Franck Legendre
  • Vincent Borrel
  • Marcelo Dias de Amorim
  • Serge Fdida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4311)

Abstract

Mobility models must scale accordingly to the application and reflect real scenarios in which wireless devices are deployed. Typical examples of scenarios requiring precise mobility models are critical situations (e.g., vehicular traffic incident, escaping pedestrians in emergency situations) – for which the ad hoc paradigm was first designed for. In these particular situations, autonomous agents of communicating devices will assist mobile users in their displacements either to avoid traffic jam due to incidents or find the closest emergency exit. But, since the environment conditions (i.e., flow of pedestrians or vehicles and incidents) may change during time in part due to mobility itself, autonomous agents assisting mobile users in their displacements must constantly exchange information and dynamically adapt to the perceived situations. This requires to precisely modeling both mobility (vehicular and pedestrian traffic) and communications systems between agents. Unfortunately, these two areas have been treated separately, although mobility and network simulators should be tightly bound. In this paper, we propose a new modeling approach to mobility, namely Behavioral Mobility models (BM), which decomposes mobility into simple atomic individual behaviors. Combined, these behaviors yield realistic displacement patterns by reproducing the mobility observed at small scales in every day life, in both space and time. We also propose to bind mobility and network simulators to run joint simulations in order to push simulations to more realness. This approach combined to BM models is particularly suited to simulate critical situations where mobility is influenced by the changing environment conditions. We demonstrate the feasibility of our approach with two cases studies.

Keywords

Mobility Model Autonomous Agent Network Simulator Behavioral Rule Emergency Team 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Camp, T., Boleng, J., Davies, V.: A Survey of Mobility Models for Ad Hoc Network Research. Wireless Communications and Mobile Computing 2(5), 483–502 (2002)CrossRefGoogle Scholar
  2. 2.
    Johnson, D., Maltz, D.: Dynamic Source Routing in Ad Hoc Wireless Networks, vol. 353. Kluwer Academic Publishers, Dordrecht (1996)Google Scholar
  3. 3.
    Hong, X., Gerla, M., Pei, G., Chiang, C.: A Group Mobility Model for Ad Hoc Wireless Networks. In: Proc. ACM/IEEE MSWiM, Seattle, WA (August 1999)Google Scholar
  4. 4.
    Sanchez, M., Manzoni, P.: Anejos: A java-based simulator for ad-hoc networks. In: Future Generation Computer Systems magazine - Elsevier (March 2001)Google Scholar
  5. 5.
    Jardosh, A., Belding-Royer, E., Almeroth, K., Suri, S.: Real World Environment Models for Mobile Ad Hoc Networks. IEEE JSAC 23(3) (March 2005)Google Scholar
  6. 6.
    McNett, M., Voelker, G.: Access and Mobility of Wireless PDA Users. ACM Mobile Computing and Communications Review 9(2), 40–55 (2005)CrossRefGoogle Scholar
  7. 7.
    Tuduce, C., Gross, T.: A Mobility Model Based on Wlan Traces and its Validation. In: Proc. IEEE INFOCOM, Miami, FL (March 2005)Google Scholar
  8. 8.
    Saha, A.K., Johnson, D.: Modeling Mobility for Vehicular Ad Hoc Networks. In: Proc. ACM VANET, Philadelphia, PA (July 2004)Google Scholar
  9. 9.
    Musolesi, M., Mascolo, C.: A Community based Mobility Model for Ad Hoc Network Research. In: Proc. ACM Realman, Florence, Italy (May 2006)Google Scholar
  10. 10.
    Ray, S., Feeley, M., Hutchinson, N., Cai, K.: Realistic mobility for mobile ad hoc network simulation (submitted for publication) (2006)Google Scholar
  11. 11.
    Kim, J., Bohacek, S.: A survey-based mobility model of people for simulation of urban mesh networks. In: MeshNets 2005, Budapest, Hungary (July 2005)Google Scholar
  12. 12.
    Kim, J., Ilic, A., Bohacek, S.: Realistic simulation of urban mesh networks - part I: Urban mobility. Technical Report, University of Delaware (2006)Google Scholar
  13. 13.
    Choffnes, D., Bustamante, F.: An integrated mobility and traffic model for vehicular wireless networks. In: Proc. ACM VANET, Cologne, Germany (September 2005)Google Scholar
  14. 14.
    Barr, R., Haas, Z., Renesse, R.V.: JiST: An efficient approach to simulation using virtual machines. IEEE Software Practice & Experience 35(6) (May 2005)Google Scholar
  15. 15.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating Dynamical Features of Escape Panic. Nature 407, 487–490 (2000)CrossRefGoogle Scholar
  16. 16.
    Yang, Q., Koutsopoulos, H.: A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transportation Research C 4(3) (1996)Google Scholar
  17. 17.
    Reynolds, C.W.: Flocks, Herds and Schools: A Distributed Behavioral Model. In: Proc. ACM SIGGRAPH, Anaheim, CA (July 1987)Google Scholar
  18. 18.
    Balakirsky, S., Messina, E.: A simulation framework for evaluating mobile robots. In: PerMIS Workshop, Gaithersburg, MD (August 2002)Google Scholar
  19. 19.
    Henderson, L.: The Statistics of Crowd Fluids. Nature 229, 381–383 (1971)CrossRefGoogle Scholar
  20. 20.
    The VINT Project. The Network simulator (Ns-2). [Online], Available: http://www.isi.edu/nsnam/ns/

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Franck Legendre
    • 1
  • Vincent Borrel
    • 1
  • Marcelo Dias de Amorim
    • 1
  • Serge Fdida
    • 1
  1. 1.Laboratoire d’Informatique de Paris 6 (LIP6/CNRS)Université Pierre et Marie CurieParis VI

Personalised recommendations