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More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource

Complex network infrastructure systems for power supply, communication, and transportation support our economic and social activities; however, they are extremely vulnerable to frequently increasing large disasters or attacks. Thus, the reconstruction of a damaged network is more advisable than an e...

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Detalles Bibliográficos
Autores principales: Hayashi, Yukio, Tanaka, Atsushi, Matsukubo, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828154/
https://www.ncbi.nlm.nih.gov/pubmed/33445680
http://dx.doi.org/10.3390/e23010102
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author Hayashi, Yukio
Tanaka, Atsushi
Matsukubo, Jun
author_facet Hayashi, Yukio
Tanaka, Atsushi
Matsukubo, Jun
author_sort Hayashi, Yukio
collection PubMed
description Complex network infrastructure systems for power supply, communication, and transportation support our economic and social activities; however, they are extremely vulnerable to frequently increasing large disasters or attacks. Thus, the reconstruction of a damaged network is more advisable than an empirically performed recovery of the original vulnerable one. To reconstruct a sustainable network, we focus on enhancing loops so that they are not trees, which is made possible by node removal. Although this optimization corresponds with an intractable combinatorial problem, we propose self-healing methods based on enhancing loops when applying an approximate calculation inspired by statistical physics. We show that both higher robustness and efficiency are obtained in our proposed methods by saving the resources of links and ports when compared to ones in conventional healing methods. Moreover, the reconstructed network can become more tolerant than the original when some damaged links are reusable or compensated for as an investment of resource. These results present the potential of network reconstruction using self-healing with adaptive capacity in terms of resilience.
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spelling pubmed-78281542021-02-24 More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource Hayashi, Yukio Tanaka, Atsushi Matsukubo, Jun Entropy (Basel) Article Complex network infrastructure systems for power supply, communication, and transportation support our economic and social activities; however, they are extremely vulnerable to frequently increasing large disasters or attacks. Thus, the reconstruction of a damaged network is more advisable than an empirically performed recovery of the original vulnerable one. To reconstruct a sustainable network, we focus on enhancing loops so that they are not trees, which is made possible by node removal. Although this optimization corresponds with an intractable combinatorial problem, we propose self-healing methods based on enhancing loops when applying an approximate calculation inspired by statistical physics. We show that both higher robustness and efficiency are obtained in our proposed methods by saving the resources of links and ports when compared to ones in conventional healing methods. Moreover, the reconstructed network can become more tolerant than the original when some damaged links are reusable or compensated for as an investment of resource. These results present the potential of network reconstruction using self-healing with adaptive capacity in terms of resilience. MDPI 2021-01-12 /pmc/articles/PMC7828154/ /pubmed/33445680 http://dx.doi.org/10.3390/e23010102 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Hayashi, Yukio
Tanaka, Atsushi
Matsukubo, Jun
More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource
title More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource
title_full More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource
title_fullStr More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource
title_full_unstemmed More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource
title_short More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource
title_sort more tolerant reconstructed networks using self-healing against attacks in saving resource
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828154/
https://www.ncbi.nlm.nih.gov/pubmed/33445680
http://dx.doi.org/10.3390/e23010102
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