About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
IoT 15(4): e3

Research Article

A Partial-differential Approximation for Spatial Stochastic Process Algebra

Download1186 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/icst.valuetools.2014.258170,
        author={Max Tschaikowski and Mirco Tribastone},
        title={A Partial-differential Approximation for Spatial Stochastic Process Algebra},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={1},
        number={4},
        publisher={EAI},
        journal_a={IOT},
        year={2015},
        month={2},
        keywords={process algebra, fluid approximation, partial differential equations},
        doi={10.4108/icst.valuetools.2014.258170}
    }
    
  • Max Tschaikowski
    Mirco Tribastone
    Year: 2015
    A Partial-differential Approximation for Spatial Stochastic Process Algebra
    IOT
    EAI
    DOI: 10.4108/icst.valuetools.2014.258170
Max Tschaikowski1,*, Mirco Tribastone1
  • 1: University of Southampton
*Contact email: m.tschaikowski@soton.ac.uk

Abstract

We study a spatial framework for process algebra with ordinary differential equation (ODE) semantics. We consider an explicit mobility model over a 2D lattice where processes may walk to neighbouring regions independently, and interact with each other when they are in same region. The ODE system size will grow linearly with the number of regions, hindering the analysis in practice. Assuming an unbiased random walk, we introduce an approximation in terms of a system of reaction-diffusion partial differential equations, of size independent of the lattice granularity. Numerical tests on a spatial version of the generalised Lotka-Volterra model show high accuracy and very competitive runtimes against ODE solutions for fine-grained lattices.

Keywords
process algebra, fluid approximation, partial differential equations
Published
2015-02-19
Publisher
EAI
http://dx.doi.org/10.4108/icst.valuetools.2014.258170

Copyright © 2015 M. Tschaikowski and M. Tribastone, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL