Fluid Analysis of Spatio-Temporal Properties of Agents in a Population Model

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9845)


We consider large stochastic population models in which heterogeneous agents are interacting locally and moving in space. These models are very common, e.g. in the context of mobile wireless networks, crowd dynamics, traffic management, but they are typically very hard to analyze, even when space is discretized in a grid. Here we consider individual agents and look at their properties, e.g. quality of service metrics in mobile networks. Leveraging recent results on the combination of stochastic approximation with formal verification, and of fluid approximation of spatio-temporal population processes, we devise a novel mean-field based approach to check such behaviors, which requires the solution of a low-dimensional set of Partial Differential Equation, which is shown to be much faster than simulation. We prove the correctness of the method and validate it on a mobile peer-to-peer network example.


Stochastic Simulation Finite Difference Scheme Continuous Time Markov Chain Absorb Boundary Condition Fast Simulation 
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.



This work was partially supported by the EU project QUANTICOL, 600708.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of Trieste and CNR/ISTIPisaItaly
  2. 2.IMT School for Advanced Studies LuccaLuccaItaly

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