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More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource.
Hayashi, Yukio; Tanaka, Atsushi; Matsukubo, Jun.
Afiliação
  • Hayashi Y; Graduate School of Science and Technology, Japan Advanced Institute of Science and Technology, Nomi 923-1292, Japan.
  • Tanaka A; Graduate School of Science and Engineering, Yamagata University, Yonezawa 992-8510, Japan.
  • Matsukubo J; Department of Creative Engineering, National Institute of Technology Kitakyushu College, Kitakyushu 802-0985, Japan.
Entropy (Basel) ; 23(1)2021 Jan 12.
Article em En | MEDLINE | ID: mdl-33445680
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Entropy (Basel) Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Entropy (Basel) Ano de publicação: 2021 Tipo de documento: Article