Statistical model checking of wireless mesh routing protocols

Peter Höfner, Annabelle McIver

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

14 Citations (Scopus)

Abstract

Several case studies indicate that model checking is limited in the analysis of mesh networks: state space explosion restricts applicability to at most 10 node networks, and quantitative reasoning, often sufficient for network evaluation, is not possible. Both deficiencies can be overcome to some extent by the use of statistical model checkers, such as SMC-Uppaal. In this paper we illustrate this by a quantitative analysis of two well-known routing protocols for wireless mesh networks, namely AODV and DYMO. Moreover, we push the limits and show that this technology is capable of analysing networks of up to 100 nodes.

Original languageEnglish
Title of host publicationNASA Formal Methods - 5th International Symposium, NFM 2013, Proceedings
EditorsGuillaume Brat, Neha Rungta, Arnaud Venet
Place of PublicationHeidelberg
PublisherSpringer, Springer Nature
Pages322-336
Number of pages15
Volume7871
ISBN (Print)9783642380877
DOIs
Publication statusPublished - 2013
Event5th International Symposium on NASA Formal Methods, NFM 2013 - Moffett Field, CA, United States
Duration: 14 May 201316 May 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7871 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th International Symposium on NASA Formal Methods, NFM 2013
CountryUnited States
CityMoffett Field, CA
Period14/05/1316/05/13

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    Höfner, P., & McIver, A. (2013). Statistical model checking of wireless mesh routing protocols. In G. Brat, N. Rungta, & A. Venet (Eds.), NASA Formal Methods - 5th International Symposium, NFM 2013, Proceedings (Vol. 7871, pp. 322-336). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7871 LNCS). Heidelberg: Springer, Springer Nature. https://doi.org/10.1007/978-3-642-38088-4_22