eprintid: 3764
rev_number: 5
eprint_status: archive
userid: 6
dir: disk0/00/00/37/64
datestamp: 2017-08-08 07:44:36
lastmod: 2017-08-08 07:44:36
status_changed: 2017-08-08 07:44:36
type: book_section
metadata_visibility: show
creators_name: Bortolussi, Luca
creators_name: Tschaikowski, Max
creators_id: 
creators_id: max.tschaikowski@imtlucca.it
title: Fluid Analysis of Spatio-Temporal Properties of Agents in a Population Model
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
abstract: 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.
date: 2016
date_type: published
series: Lecture Notes in Computer Science
number: 9845
publisher: Springer
pagerange: 92-106
id_number: 10.1007/978-3-319-43904-4_7
refereed: TRUE
isbn: 978-3-319-43904-4
book_title: Analytical and Stochastic Modelling Techniques and Applications. ASMTA 2016
official_url: https://doi.org/10.1007/978-3-319-43904-4_7
projects: This work was partially supported by the EU project QUANTICOL, 600708
citation:   Bortolussi, Luca and Tschaikowski, Max  Fluid Analysis of Spatio-Temporal Properties of Agents in a Population Model.   In:  Analytical and Stochastic Modelling Techniques and Applications. ASMTA 2016.   Lecture Notes in Computer Science  (9845).  Springer, pp. 92-106.  ISBN 978-3-319-43904-4      (2016)