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Title ENRN: A System for Evaluating Network Resilience against Natural Disasters
ID_Doc 44545
Authors Alenazi, MJF
Title ENRN: A System for Evaluating Network Resilience against Natural Disasters
Year 2023
Published Mathematics, 11, 20
DOI 10.3390/math11204250
Abstract The frequency and severity of natural disasters is surging, posing an urgent need for robust communication network infrastructure that is capable of withstanding these events. In this paper, we present a groundbreaking graph-theoretic system designed to evaluate and enhance network resilience in the face of natural disasters. Our solution harnesses the power of topological robustness metrics, integrating real-time weather data, geographic information, detailed network topology data, advanced resilience algorithms, and continuous network monitoring. The proposed scheme considers four major real-world U.S.-based network providers and evaluates their physical topologies against two major hurricanes. Our novel framework quantifies the important characteristics of network infrastructure; for instance, AT&T is identified to have fared better against Hurricane Ivan (57.98 points) than Hurricane Katrina (39.17 points). We not only provide current insights into network infrastructure resilience, but also uncover valuable findings that shed light on the performance of backbone U.S. networks during hurricanes. Furthermore, our findings provide actionable insights to enrich the overall survivability and functionality of communication networks, mitigating the adverse impacts of natural disasters on communication systems and critical services in terms of improving network resiliency via adding additional nodes and link or rewiring.
Author Keywords network resilience; graph theory; smart city; communication networks; 94-10
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Science Citation Index Expanded (SCI-EXPANDED)
EID WOS:001093573200001
WoS Category Mathematics
Research Area Mathematics
PDF https://www.mdpi.com/2227-7390/11/20/4250/pdf?version=1697030034
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