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dc.date.accessioned2014-06-19T13:56:23Z
dc.date.available2014-06-19T13:56:23Z
dc.date.created2014-06-19T09:17:21Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10852/39285
dc.description.abstractUnbounded data structures, advanced functions and data types, and/or different forms of communication are often needed to model large and complex probabilistic real-time systems such as wireless sensor network algorithms. Furthermore, it is natural to model distributed probabilistic real-time systems in an object-oriented style, including using subclass inheritance and dynamic object and message creation and deletion. To support the above features, we introduce probabilistic real-time rewrite theories (PRTRTs), that extend both real-time rewrite theories and probabilistic rewrite theories, as a rewriting-logic-based formalism for probabilistic real-time systems. We then show that PRTRTs can be seen as a unifying model in which a range of other models for probabilistic real-time systems---including probabilistic timed automata, stochastic automata, deterministic and stochastic Petri nets, as well as two probabilistic timed transition system models with underspecified probability distributions---can naturally be represented. We also provide semantics-preserving mappings from these models into PRTRTs, and prove their correctness. Finally, we show how the OGDC state-of-the-art algorithm for wireless sensor network algorithm can be specified in our formalism.en_US
dc.languageEN
dc.language.isoenen_US
dc.publisherUniversitetet i Oslo
dc.relation.ispartofResearch report http://urn.nb.no/URN:NBN:no-35645
dc.relation.urihttp://urn.nb.no/URN:NBN:no-35645
dc.titleProbabilistic Real-Time Rewrite Theories and Their Expressive Poweren_US
dc.typeResearch reporten_US
dc.creator.authorBentea, Lucian
dc.creator.authorØlveczky, Peter Csaba
cristin.unitcode185,15,5,32
cristin.unitnamePresis modellering og analyse
cristin.ispublishedtrue
cristin.fulltextoriginal
dc.identifier.cristin1139007
dc.identifier.pagecount44
dc.identifier.urnURN:NBN:no-44156
dc.type.documentForskningsrapporten_US
dc.source.isbn82-7368-395-8
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/39285/1/RR430-IFI.pdf


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