Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks

Autores
D'argenio, Pedro Ruben; Fraire, Juan Andres; Hartmanns, Arnd; Raverta, Fernando Dario
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In delay-tolerant networks (DTNs) with uncertain contact plans, the communication episodes and their reliabilities are known a priori. To maximise the end-to-end delivery probability, a bounded network-wide number of message copies are allowed. The resulting multi-copy routing optimization problem is naturally modelled as a Markov decision process with distributed information. In this paper, we provide an in-depth comparison of three solution approaches: statistical model checking with scheduler sampling, the analytical RUCoP algorithm based on probabilistic model checking, and an implementation of concurrent Q-learning. We use an extensive benchmark set comprising random networks, scalable binomial topologies, and realistic ring-road low Earth orbit satellite networks. We evaluate the obtained message delivery probabilities as well as the computational effort. Our results show that all three approaches are suitable tools for obtaining reliable routes in DTN, and expose a tradeoff between scalability and solution quality.
Fil: D'argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Fraire, Juan Andres. Institut National de Recherche en Informatique et en Automatique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina
Fil: Hartmanns, Arnd. Universiteit Twente (ut);
Fil: Raverta, Fernando Dario. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Materia
DELAY TOLERANT NETWORKS
STATISTICAL MODEL CHECKING
Q-LEARNING
ROUTING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/273623
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant NetworksD'argenio, Pedro RubenFraire, Juan AndresHartmanns, ArndRaverta, Fernando DarioDELAY TOLERANT NETWORKSSTATISTICAL MODEL CHECKINGQ-LEARNINGROUTINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In delay-tolerant networks (DTNs) with uncertain contact plans, the communication episodes and their reliabilities are known a priori. To maximise the end-to-end delivery probability, a bounded network-wide number of message copies are allowed. The resulting multi-copy routing optimization problem is naturally modelled as a Markov decision process with distributed information. In this paper, we provide an in-depth comparison of three solution approaches: statistical model checking with scheduler sampling, the analytical RUCoP algorithm based on probabilistic model checking, and an implementation of concurrent Q-learning. We use an extensive benchmark set comprising random networks, scalable binomial topologies, and realistic ring-road low Earth orbit satellite networks. We evaluate the obtained message delivery probabilities as well as the computational effort. Our results show that all three approaches are suitable tools for obtaining reliable routes in DTN, and expose a tradeoff between scalability and solution quality.Fil: D'argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Fraire, Juan Andres. Institut National de Recherche en Informatique et en Automatique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaFil: Hartmanns, Arnd. Universiteit Twente (ut);Fil: Raverta, Fernando Dario. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaAssociation for Computing Machinery2025-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/273623D'argenio, Pedro Ruben; Fraire, Juan Andres; Hartmanns, Arnd; Raverta, Fernando Dario; Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks; Association for Computing Machinery; Acm Transactions On Modeling And Computer Simulation; 35; 2; 4-2025; 1-261049-3301CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://dl.acm.org/doi/10.1145/3665927info:eu-repo/semantics/altIdentifier/doi/10.1145/3665927info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:46:35Zoai:ri.conicet.gov.ar:11336/273623instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:46:35.573CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks
title Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks
spellingShingle Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks
D'argenio, Pedro Ruben
DELAY TOLERANT NETWORKS
STATISTICAL MODEL CHECKING
Q-LEARNING
ROUTING
title_short Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks
title_full Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks
title_fullStr Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks
title_full_unstemmed Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks
title_sort Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks
dc.creator.none.fl_str_mv D'argenio, Pedro Ruben
Fraire, Juan Andres
Hartmanns, Arnd
Raverta, Fernando Dario
author D'argenio, Pedro Ruben
author_facet D'argenio, Pedro Ruben
Fraire, Juan Andres
Hartmanns, Arnd
Raverta, Fernando Dario
author_role author
author2 Fraire, Juan Andres
Hartmanns, Arnd
Raverta, Fernando Dario
author2_role author
author
author
dc.subject.none.fl_str_mv DELAY TOLERANT NETWORKS
STATISTICAL MODEL CHECKING
Q-LEARNING
ROUTING
topic DELAY TOLERANT NETWORKS
STATISTICAL MODEL CHECKING
Q-LEARNING
ROUTING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In delay-tolerant networks (DTNs) with uncertain contact plans, the communication episodes and their reliabilities are known a priori. To maximise the end-to-end delivery probability, a bounded network-wide number of message copies are allowed. The resulting multi-copy routing optimization problem is naturally modelled as a Markov decision process with distributed information. In this paper, we provide an in-depth comparison of three solution approaches: statistical model checking with scheduler sampling, the analytical RUCoP algorithm based on probabilistic model checking, and an implementation of concurrent Q-learning. We use an extensive benchmark set comprising random networks, scalable binomial topologies, and realistic ring-road low Earth orbit satellite networks. We evaluate the obtained message delivery probabilities as well as the computational effort. Our results show that all three approaches are suitable tools for obtaining reliable routes in DTN, and expose a tradeoff between scalability and solution quality.
Fil: D'argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Fraire, Juan Andres. Institut National de Recherche en Informatique et en Automatique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina
Fil: Hartmanns, Arnd. Universiteit Twente (ut);
Fil: Raverta, Fernando Dario. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
description In delay-tolerant networks (DTNs) with uncertain contact plans, the communication episodes and their reliabilities are known a priori. To maximise the end-to-end delivery probability, a bounded network-wide number of message copies are allowed. The resulting multi-copy routing optimization problem is naturally modelled as a Markov decision process with distributed information. In this paper, we provide an in-depth comparison of three solution approaches: statistical model checking with scheduler sampling, the analytical RUCoP algorithm based on probabilistic model checking, and an implementation of concurrent Q-learning. We use an extensive benchmark set comprising random networks, scalable binomial topologies, and realistic ring-road low Earth orbit satellite networks. We evaluate the obtained message delivery probabilities as well as the computational effort. Our results show that all three approaches are suitable tools for obtaining reliable routes in DTN, and expose a tradeoff between scalability and solution quality.
publishDate 2025
dc.date.none.fl_str_mv 2025-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/273623
D'argenio, Pedro Ruben; Fraire, Juan Andres; Hartmanns, Arnd; Raverta, Fernando Dario; Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks; Association for Computing Machinery; Acm Transactions On Modeling And Computer Simulation; 35; 2; 4-2025; 1-26
1049-3301
CONICET Digital
CONICET
url http://hdl.handle.net/11336/273623
identifier_str_mv D'argenio, Pedro Ruben; Fraire, Juan Andres; Hartmanns, Arnd; Raverta, Fernando Dario; Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks; Association for Computing Machinery; Acm Transactions On Modeling And Computer Simulation; 35; 2; 4-2025; 1-26
1049-3301
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://dl.acm.org/doi/10.1145/3665927
info:eu-repo/semantics/altIdentifier/doi/10.1145/3665927
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Association for Computing Machinery
publisher.none.fl_str_mv Association for Computing Machinery
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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