Robustness of Interdependent Random Geometric Networks
Name
percolation.pdf
Description
Accepted version
Size
1.17 MB
Format
Adobe PDF
Checksum (MD5)
5a58b2adca685f4928810ace562c44b8
Author(s)
Zhang, Jianan
Modiano, Eytan H
Date Issued
July 2019
Journal
IEEE transactions on network science and engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhang, Jianan, Edmund Yeh and Eytan Modiano. “Robustness of Interdependent Random Geometric Networks.” IEEE transactions on network science and engineering, vol. 6, no. 3, 2019, pp. 474-487 © 2019 The Author(s)
Version
Author's final manuscript
Abstract
We propose an interdependent random ggraph (RGG) model for interdependent networks. Based on this model, we study the robustness of two interdependent spatially embedded networks where interdependence exists between geographically nearby nodes in the two networks. We study the emergence of the giant mutual component in two interdependent RGGs as node densities increase, and define the percolation threshold as a pair of node densities above which the giant mutual component first appears. In contrast to the case for a single RGG, where the percolation threshold is a unique scalar for a given connection distance, for two interdependent RGGs, multiple pairs of percolation thresholds may exist, given that a smaller node density in one RGG may increase the minimum node density in the other RGG in order for a giant mutual component to exist. We derive analytical upper bounds on the percolation thresholds of two interdependent RGGs by discretization, and obtain 99 percent confidence intervals for the percolation thresholds by simulation. Based on these results, we derive conditions for the interdependent RGGs to be robust under random failures and geographical attacks.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/TNSE.2018.2846720
