Distributed Bayesian Diagnosis for Telecommunication Networks

Sedano-Frade, Andrés; Gonzalez-Ordás, Javier; Arozarena-Llopis, Pablo; García-Gómez, Sergio; Carrera, Álvaro

doi:10.1007/978-3-642-12384-9_28

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…We focused on the deliberation process, leaving outside other research scope issues. Our proposal of decision support improves previous approaches [7], [8] both in time and in computational cost.…”

Section: Discussionmentioning

confidence: 59%

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Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

Carrera

Iglesias

2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-This article proposes a MAS architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypothesis generation and hypothesis confirmation. The first process is distributed among several agents based on a MSBN, while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been defined in order to combine both inference processes.To drive the deliberation process, dynamic data about the influence of observations are taken during diagnosis process. In order to achieve quick and reliable diagnoses, this influence is used to choose the best action to perform. This approach has been evaluated in a P2P video streaming scenario. Computational and time improvements are highlight as conclusions.

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Section: Discussionmentioning

confidence: 59%

“…The benefits of the proposed meta-model have been evaluated comparing this approach with previous works [7], [8]. In this paper, we compare the performance of the system using deliberation driven by "cost" or by "influence".…”

Section: Discussionmentioning

confidence: 99%

Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

Carrera

Iglesias

2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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“…Figura 3: Red bayesiana de fibIT En fibIT se usan redes bayesianas (Pearl, 1985) con el motor de razonamiento proporcionado por la plataforma de desarrollo propietario KOWGAR (Sedano et al, 2010). La figura 3 muestra la red principal para el diagnóstico de conectividad FTTH.…”

Section: Red Bayesianaunclassified

Un enfoque probabilístico en la autorreparación de redes G-PON

García-Algarra

Gonzalez-Ordás

Arozarena

et al. 2014

Revista Iberoamericana de Automática e Informática Industrial R

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El despliegue de accesos de fibra óptica hasta los hogares (Fiber To The Home, FTTH en adelante) es una prioridad de los operadores de telecomunicación para soportar nuevos servicios digitales y mejorar la experiencia de los usuarios. G-PON es la tecnología más común; su instalación plantea importantes retos en el diagnóstico y reparación de averías de esta infraestructura, de características muy diferentes a las de los tradicionales pares de cobre. En este artículo presentamos una experiencia basada en un enfoque probabilístico del problema.

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“…Efficient monitoring, control and management of telecommunication networks [9], and network fault diagnosis, analysis and predictions [10,11] are two main categories of applications of Bayesian network formalism in mobile communications. In addition to the above applications, we introduced a new application of Bayesian networks for modeling user preferences for radio access selection [12,13] which was later extended [14,15].…”

Section: Bayesian Networkmentioning

confidence: 99%

A Bayesian Approach to Service Selection for Secondary Users in Cognitive Radio Networks

Homayounvala¹

2015

ijacsa

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Abstract-In cognitive radio networks where secondary users (SUs) use the time-frequency gaps of primary users' (PUs) licensed spectrum opportunistically, the experienced throughput of SUs depend not only on the traffic load of the PUs but also on the PUs' service type. Each service has its own pattern of channel usage, and if the SUs know the dominant pattern of primary channel usage, then they can make a better decision on choosing which service is better to be used at a specific time to get the best advantage of the primary channel, in terms of higher achievable throughput. However, it is difficult to inform directly SUs of PUs' dominant used services in each area, for practical reasons. This paper proposes a learning mechanism embedded in SUs to sense the primary channel for a specific length of time. This algorithm recommends the SUs upon sensing a free primary channel, to choose the best service in order to get the best performance, in terms of maximum achieved throughput and the minimum experienced delay. The proposed learning mechanism is based on a Bayesian approach that can predict the performance of a requested service for a given SU. Simulation results show that this service selection method outperforms the blind opportunistic SU service selection, significantly.

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Distributed Bayesian Diagnosis for Telecommunication Networks

Cited by 5 publications

References 7 publications

Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

Un enfoque probabilístico en la autorreparación de redes G-PON

A Bayesian Approach to Service Selection for Secondary Users in Cognitive Radio Networks

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