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Vertex-Connectivity for Node Failure Identification in Boolean Network Tomography

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Algorithms for Sensor Systems (ALGOSENSORS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11931))

Abstract

In this paper we study the node failure identification problem in undirected graphs by means of Boolean Network Tomography. We argue that vertex connectivity plays a central role. We show tight bounds on the maximal identifiability in a particular class of graphs, the Line of Sight networks. We prove slightly weaker bounds on arbitrary networks. Finally we initiate the study of maximal identifiability in random networks. We focus on two models: the classical Erdős-Rényi model, and that of Random Regular graphs. The framework proposed in the paper allows a probabilistic analysis of the identifiability in random networks giving a tradeoff between the number of monitors to place and the maximal identifiability.

The first two authors kindly acknowledge the partial support by the MIUR under the grant “Dipartimenti di eccellenza 2018–2022” of the Department of Computer Science of Sapienza University. The research was also partly supported by a visiting fellowship of the University of Liverpool and the Networks Sciences & Technologies (NeST) initiative of the University of Liverpool (https://www.liverpool.ac.uk/network-science-technologies/).

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Authors and Affiliations

  1. Universitá La Sapienza, Rome, Italy

    Nicola Galesi & Fariba Ranjbar

  2. University of Liverpool, Liverpool, UK

    Michele Zito

Authors
  1. Nicola Galesi
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  2. Fariba Ranjbar
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  3. Michele Zito
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Corresponding author

Correspondence to Michele Zito .

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Editors and Affiliations

  1. Paderborn University, Paderborn, Germany

    Falko Dressler

  2. Paderborn University, Paderborn, Germany

    Christian Scheideler

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Galesi, N., Ranjbar, F., Zito, M. (2019). Vertex-Connectivity for Node Failure Identification in Boolean Network Tomography. In: Dressler, F., Scheideler, C. (eds) Algorithms for Sensor Systems. ALGOSENSORS 2019. Lecture Notes in Computer Science(), vol 11931. Springer, Cham. https://doi.org/10.1007/978-3-030-34405-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-34405-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34404-7

  • Online ISBN: 978-3-030-34405-4

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