Vulnerability and Resilience Analysis of the Air Traffic Control Sector Network in China

Wang, Xinglong; Miao, Shiguang; Tang, Junqing

doi:10.3390/su12093749

Cited by 23 publications

(19 citation statements)

References 58 publications

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“…Another proposal is presented in [34] on the basis of the theory of structural holes [35], and assumes the modification of the network topology by appropriately adding new links (link-addition strategy) without knowing the global topology of the graph, but relying only on local information. However, in analyzing the resilience of graphs, the topological approach has been applied in many contexts such as, for instance, mobile communication infrastructures [36], air traffic networks [37], P2P networks [38], and many others.…”

Section: Related Workmentioning

confidence: 99%

Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis

et al. 2021

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Alzheimer’s Disease (AD) is an incurable neurodegenerative disorder which mainly affects older adults. An early diagnosis is essential because medical treatments can slow down the progression of the disease only if provided during the first stage, called Mild Cognitive Impairment (MCI). Starting from the study of electroencephalografic signals, brain functional connectivity analyses can be performed with the support of the graph theory. In particular, the purpose of this work is to verify the performances of three indexes, typically adopted to evaluate the graph robustness, in order to estimate the functional connectivity for three groups of subjects: healthy controls and people affected by dementia at two different stages (MCI and AD). The results obtained by the Connection Density Index, the Randić Index, and a normalized version of the Kirchhoff Index revealed a higher robustness in the brain networks of healthy people, followed by MCI and, finally, by AD patients, consistent with the hallmarks of Alzheimer’s disease. The statistical analysis showed that there is a significant difference between controls and AD for all three indexes. Finally, all three indexes were compared, revealing that the the Randić Index outperformed the other two indexes. These preliminary outcomes will be exploited to address further in-depth and time-expensive analyses for improving the diagnosis of Alzheimer’s disease.

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Section: Related Workmentioning

confidence: 99%

Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis

et al. 2021

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“…During the disruptions, more vulnerable CISs will experience more severe failure [144]. Although there is no consensus about the relationship between vulnerability and resilience, it seems that higher vulnerability of the CIS leads to lower resiliency and vice versa [9,107].…”

Section: What Types Of Terms Define/determine the Resilience Of Technmentioning

confidence: 99%

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

et al. 2021

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Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases.

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“…ey are based on traffic volume and random and network centrality (eigenvector, closeness, betweenness, and degree), respectively. Wang et al [28] compared the effects of random, degree-based, betweenness-based, and remove sequence-based recover strategy in China ATC sector network. However, to the best of our knowledge, we have not noticed much progress in recover strategies.…”

Section: Attack and Recovermentioning

confidence: 99%

“…Flight delay will occur frequently and propagate heavily if air transportation network is vulnerable, less robust, or less resilient to disturbances. Network efficiency [21] and size of the largest connected subgraph [22] became the most frequent utilized indicators in robustness description of air transportation network [23][24][25][26][27][28] and both of them come from complex network. Indices, including degree, betweenness, clustering coefficients, and closeness, could be employed to make effective and efficient attack/recover strategy [23][24][25][26][27][28] to network resilience.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress about Flight Delay under Complex Network

2021

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Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important nodes/edges. And, we made a clarification about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. (1) Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. (2) Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. (3) It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. (4) Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed.

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Vulnerability and Resilience Analysis of the Air Traffic Control Sector Network in China

Cited by 23 publications

References 58 publications

Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis

Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

Recent Progress about Flight Delay under Complex Network

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