A connection probability model for communications networks under regional failures

Kabir, Muhammad Nomani; Rahman, Arafatur; Azad, Saiful; Azim, Mohamed Mostafa A.; Bhuiyan, Zakirul Alam

doi:10.1016/j.ijcip.2017.11.002

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to analyze the attacks targeting the critical areas in a network and identify the vulnerabilities, the authors in [24] formulated the Critical Node Identification problem and the Critical Area Identification problem to find the critical node and the critical area under regional attacks or failures. To consider each link's connection under regional/area attacks, the authors in [25] estimated the connection probability of each link when there is an area-based attacks, but the method does not consider the global connectivity, which is of great importance to a system. More importantly, AI technology has prominent advantage in the aspect of identifying critical areas in the heterogeneous IoV systems [26], but there is no suitable solution in the existing researches.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Vulnerability Analysis for Connectivity and Critical-Area Integrity in IoV

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The large-scale connectivity of Internet of Vehicles (IoV) is an important challenge for the Intelligent Transportation Systems (ITS). Intelligence vulnerability analysis is an excellent solution. However, existing methods for analyzing connectivity vulnerability have ignored the existence of critical areas in the system. Due to the heterogeneities of the IoV environments and services, the failure of some specific areas may seriously damage connectivity and system performance. To this end, in this paper we focus on both the dynamic connectivity and the critical-area integrity, and propose an intelligent vulnerability analysis method to effectively identify the critical area of extreme vulnerability. Specifically, we consider an intelligent analysis scenario in which roadside servers continuously learn IoV heterogeneous environment and dynamic topology, and then translate the learning results into a flexible disruption cost problem. Based on this, we utilize the spectral partitioning method to identify the minimum-cost set of topological elements whose failure not only severely damages system connectivity but also disrupts its critical areas. Furthermore, we confirm that the identified set can be used to optimize disruption cost problem, thus intelligently improving vulnerability. Simulation results show that our proposed method can effectively identify vulnerable elements and prevent significant loss in the IoV system connectivity and performance. INDEX TERMS Intelligent Transportation Systems (ITS), Internet of Vehicles (IoV), intelligence vulnerability analysis, connectivity, critical area

show abstract

Section: Related Workmentioning

confidence: 99%

Intelligent Vulnerability Analysis for Connectivity and Critical-Area Integrity in IoV

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In reality, the resilience of infrastructure networks is often affected by random failures or intentional attacks, for example, the large-scale blackout in America, the paralysis of the railway network and the power grid in China due to natural disasters, and so on. To this end, there are many works focusing on infrastructure systems [ 1 – 8 ], communication networks [ 9 – 11 ], and supply networks [ 12 , 13 ]. Because the load on a node over the capacity causes the failure propagation, how to allocate initial loads is closely related to the robustness of networks.…”

Section: Introductionmentioning

confidence: 99%

Modelling cascading failures in networks with the harmonic closeness

et al. 2021

View full text Add to dashboard Cite

Many studies on cascading failures adopt the degree or the betweenness of a node to define its load. From a novel perspective, we propose an approach to obtain initial loads considering the harmonic closeness and the impact of neighboring nodes. Based on simulation results for different adjustable parameter θ, local parameter δ and proportion of attacked nodes f, it is found that in scale-free networks (SF networks), small-world networks (SW networks) and Erdos-Renyi networks (ER networks), there exists a negative correlation between optimal θ and δ. By the removal of the low load node, cascading failures are more likely to occur in some cases. In addition, we find a valuable result that our method yields better performance compared with other methods in SF networks with an arbitrary f, SW and ER networks with large f. Moreover, the method concerning the harmonic closeness makes these three model networks more robust for different average degrees. Finally, we perform the simulations on twenty real networks, whose results verify that our method is also effective to distribute the initial load in different real networks.

show abstract

“…An overloaded sensitive edge of a power grid can lead to capacity loss in the edge, and an overloaded edge in the central flow of the network can result in cascading breakdowns [ 11 ]. In communication networks, damage to optical fiber cables can partially overload data delivery, resulting in a regional interruption of Internet services [ 12 ]. In biological neural networks, edge failure such as the malfunction of neural conduction can cause a local or systemic paralysis.…”

Section: Introductionmentioning

confidence: 99%

Resilience of and recovery strategies for weighted networks

Pan

Wang

2018

PLoS ONE

View full text Add to dashboard Cite

The robustness and resilience of complex networks have been widely studied and discussed in both research and industry because today, the diversity of system components and the complexity of the connection between units are increasingly influencing the reliability of complex systems. Previous studies have focused on node failure in networks, proposing several performance indicators. However, a single performance indicator cannot comprehensively measure all the performance aspects; thus, the selected performance indicators and recovery strategies lack consistency with respect to the targeted complex systems. This paper introduces a novel stress–strength-balanced weighted network model based on two network transmission hypotheses. Then, with respect to different concerns of the complex network, we propose two modified network performance measurement indicators and compare these indicators in terms of their trends in the attack process. In addition, we introduce several network recovery strategies and compare their efficiencies. Our findings are as follows: (1) The evaluation and judgment of the network performance depend on the performance measurement indicators we use. (2) Different recovery strategies exhibit distinct efficiencies in recovering different aspects of network performance, and no strategy exists that can improve all the network performance aspects simultaneously. (3) The timing of the recovery is proved to have a deep influence on the cost and efficiency of network recovery; thus, the optimal recovery strategy for a damaged network varies with the extent of the damage. From the results of the simulation of the attack-recovery process, we conclude that while defining and analyzing complex network models, we should adjust our network topology, weight assignment, and performance indicators in accordance with the focal characteristics of complex systems so that we can use the network model to build robust complex systems and efficient logistics and maintenance strategies.

show abstract

A connection probability model for communications networks under regional failures

Cited by 6 publications

References 14 publications

Intelligent Vulnerability Analysis for Connectivity and Critical-Area Integrity in IoV

Intelligent Vulnerability Analysis for Connectivity and Critical-Area Integrity in IoV

Modelling cascading failures in networks with the harmonic closeness

Resilience of and recovery strategies for weighted networks

Contact Info

Product

Resources

About