ENRN: A System for Evaluating Network Resilience against Natural Disasters

Mohammed J. F. Alenazi ()
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Mohammed J. F. Alenazi: Department of Computer Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11451, Saudi Arabia

Mathematics, 2023, vol. 11, issue 20, 1-23

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.

Keywords: network resilience; graph theory; smart city; communication networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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