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)


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.


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.


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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

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