Mogyorósi, Ferenc and Revisnyei, Péter and Pašić, Alija (2025) Modeling and upgrade of disaster-resilient interdependent networks using machine learning. OPTICAL SWITCHING AND NETWORKING, 55. No. 100791. ISSN 1573-4277
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Abstract
Recent global emergencies emphasize the critical role of reliable communication networks. As dependence on critical infrastructures grows, the focus shifts from isolated failures to designing networks capable of withstanding disasters, taking into account their interdependence with infrastructures like the power grid. This paper investigates the problem of the disaster resilient upgrade of interdependent networks, focusing on enhancing network resilience during emergencies and ensuring a service-level agreement. We analyze how the interdependency between the networks affects the disaster resilience and propose heuristic methods for network operators to improve resilience against disasters. Furthermore, to address the challenge of hidden interdependencies, we present a novel approach using graph neural networks for predicting interdependency between networks based on historical data of failures. Using simulations with real networks and earthquake data, we demonstrate that limiting the number of interdependent edges per node significantly affects resilience. We show that if sufficient data is available graph neural networks can learn the connection between failures and interdependencies, and capable of predicting interdependencies. Additionally, we show that selecting appropriate upgrade methods can reduce network upgrade costs by up to 20%. © 2024
| Item Type: | Article |
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| Additional Information: | Export Date: 25 November 2024 Correspondence Address: Mogyorósi, F.; Department of Telecommunications and Media Informatics, Műegyetem rkp. 3., Budapest, Hungary; email: mogyorosi@tmit.bme.hu Funding details: Innovációs és Technológiai Minisztérium Funding details: Magyar Tudományos Akadémia, MTA Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA, 137698, KDP-2021, 146127, ÚNKP-23-5-BME-451, PD_21 Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA Funding text 1: Project no. C1445813 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2021 funding scheme. Projects no. 137698 and 146127 have been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the PD_21, and FK_23 funding schemes, respectively. The work A. Pa\\u0161i\\u0107 was supported by the \\u00DANKP-23-5-BME-451 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund. This paper was supported by the J\\u00E1nos Bolyai Research Scholarship of the Hungarian Academy of Sciences. |
| Uncontrolled Keywords: | machine learning; Machine-learning; Power grids; Disasters; Reliable communication; INTERDEPENDENT NETWORKS; network resilience; Interdependent network; Disaster resilience; Communications networks; Graph neural networks; Graph neural networks; Graph neural networks; adversarial machine learning; Servicelevel agreement (SLA); Disaster resiliences; Interdependency prediction; Interdependency prediction; |
| Subjects: | T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 08 Oct 2025 05:46 |
| Last Modified: | 08 Oct 2025 05:46 |
| URI: | https://real.mtak.hu/id/eprint/226151 |
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