Interdependencies and Cascading Effects of Disasters on Critical Infrastructures: An Analysis of Base Station Communication Networks

Eva Lee, Eva Lee, Eva Lee, William Wang

2023

Abstract

There are sixteen critical infrastructure (CI) sectors whose assets, systems, and networks, whether physical or virtual, are considered so vital to the United States that their incapacitation or destruction would have a debilitating effect on military readiness, economic security, public health, or safety. The communications sector is unique as a critical infrastructure sector due to its central role in facilitating the flow of information, enabling communication, and supporting all other CIs as well as other components of the economy and society. Within the communications sector, the cellular base station (cell tower) network serves as its foundational backbone. During a crisis, if towers in the network stop functioning or are damaged, the service load of associated users/businesses will have to be transferred to other towers, potentially causing congestion and cascading effects of overload service outages and vulnerabilities. In this paper, we investigate cellular base station network vulnerability by uncovering the most critical nodes in the network whose collapse would trigger extreme cascading effects. We model the cellular base station network via a linear-threshold influence network, with the objective of maximizing the spread of influence. A two-stage approach is proposed to determine the set of critical nodes. The first stage clusters the nodes geographically to form a set of sub-networks. The second stage simulates congestion propagation by solving an influence maximization problem on each sub-network via a greedy Monte Carlo simulation and a heuristic Simpath algorithm. We also identify the cascading nodes that could run into failure if critical nodes fail. The results offer policymakers insight into allocating resources for maximum protection and resiliency against natural disasters or attacks by terrorists or foreign adversaries. We extend the model to the weighted LT influence network (WLT-IN) and prove that the associated influence function is monotone and submodular. We also demonstrate an adaptable usage of WLT-IN for airport risk assessment and biological intelligence of COVID 19 disease spread and its scope of impact to air transportation, economy, and population health.

Paper Citation

in Harvard Style

Lee E. and Wang W. (2023). Interdependencies and Cascading Effects of Disasters on Critical Infrastructures: An Analysis of Base Station Communication Networks. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 141-152. DOI: 10.5220/0012239600003598

in Bibtex Style

@conference{kdir23,
author={Eva Lee and William Wang},
title={Interdependencies and Cascading Effects of Disasters on Critical Infrastructures: An Analysis of Base Station Communication Networks},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={141-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012239600003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Interdependencies and Cascading Effects of Disasters on Critical Infrastructures: An Analysis of Base Station Communication Networks
SN - 978-989-758-671-2
AU - Lee E.
AU - Wang W.
PY - 2023
SP - 141
EP - 152
DO - 10.5220/0012239600003598
PB - SciTePress