NDLI: A 3-layered self-reconfigurable generic model for self-diagnosis of telecommunication networks

Content Provider	IEEE Xplore Digital Library
Author	Tembo, S.R. Courant, J.-L. Vaton, S.
Copyright Year	2015
Description	Author affiliation: Orange Labs., Lannion, France (Tembo, S.R.; Courant, J.-L.) \|\| Telecom Bretagne, Brest, France (Vaton, S.)
Abstract	The dynamic and distributed nature of telecommunication networks makes complex the design of model-based approaches for network fault diagnosis. Most model-based approaches assume the prior existence of the model which is reduced to a static image of the network. Such models become rapidly obsolete when the network changes. We propose in this paper a 3-layered self-reconfigurable generic model of fault diagnosis in telecommunication networks. The layer 1 of the model is an undirected graph which models the network topology. Network behavior, also called fault propagation, is modeled in layer 2 using a set of directed acyclic graphs interconnected via the layer 1. We handle uncertainties of fault propagation by quantifying strengths of dependencies between layer 2 nodes with conditional probability distributions estimated from network generated data. Layer 3 is the junction tree representation of the loopy obtained layer 2 Bayesian networks. The junction tree is the diagnosis computational layer since exact inference algorithms fail on loopy Bayesian networks. This generic model embeds intelligent self-reconfiguration capabilities in order to track some changes in network topology and network behavior. These self-reconfiguration capabilities are highlighted through some example scenarios that we describe. We apply this 3-layered generic model to carry out active self-diagnosis of the GPON-FTTH access network. We present and analyze some experimental diagnosis results carried out by running a Python implementation of the generic model.
Starting Page	25
Ending Page	34
File Size	695615
Page Count	10
File Format	PDF
e-ISBN	9781467376068
DOI	10.1109/IntelliSys.2015.7361080
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-10
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	FTTH Computational modeling Probabilistic logic GPON Cognition Fault propagation Fault diagnosis Network topology Bismuth Probabilistic Inference Self-reconfiguration Self-diagnosis Bayesian network
Content Type	Text
Resource Type	Article

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