Improving the Survivability of Clustered Interdependent Networks by Restructuring Dependencies

Ishigaki, Genya; Gour, Riti; Jue, Jason P.

doi:10.1109/tcomm.2018.2889983

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…Two metaheuristic methods for deriving the interdependency between the components in the cyber physical systems were proposed [27]. An efficient method for restructuring the interdependency has been proposed to improve the survivability of the interdependent networks [28]. However, the above three works only considered the cases where the topology of the individual networks is unknown.…”

Section: Related Workmentioning

confidence: 99%

Robust Design of Interdependent Networks Considering Intra-Network Support Flow

Ogino

2023

IEEE Trans. Netw. Serv. Manage.

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Section: Related Workmentioning

confidence: 99%

Robust Design of Interdependent Networks Considering Intra-Network Support Flow

Ogino

2023

IEEE Trans. Netw. Serv. Manage.

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“…Two metaheuristic methods for deriving the optimum interdependency between the components in the cyber physical systems were proposed [27]. An efficient method for restructuring the interdependency has been proposed to improve the survivability of the interdependent networks [28]. However, the above three literatures only considered the cases where the topology of each of the networks is unknown.…”

Section: Related Workmentioning

confidence: 99%

Robust Design of Interdependent Networks Considering Intra-Network Support Flow

Ogino¹

2022

Preprint

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<p>Cyber-physical systems comprise multiple interdependent networks supporting each other. A node in an interdependent network fails when it cannot receive a sufficient volume of support from normal nodes in the other interdependent networks. Meanwhile, each node loses its support capability if it cannot terminate intra-network flow named “support flow”, which corresponds to the flow of electric power in the power grid and the flow of control messages in the computer network. This means that each sink node of the support flow is regarded as being in a failure state when it is disconnected with all the normal source nodes of the support flow in the same network. This criterion of the sink node failure increases the possibility of an initial node failure invoking a cascading failure with a wide range. This paper proposes a robust design method for the interdependent networks that takes the support flow into account. The proposed method can derive the optimum interdependency to minimize the greatest impact of the cascading failures induced from a given set of the initial node failures. The robust design problem is formulated using a mixed integer linear programming model and two kinds of metaheuristic methods are proposed to solve the problem efficiently even when numerous initial node failures are specified. Several characteristics of the risk of the cascading failures are revealed from the results of the proposed robust design. Simulation experiments demonstrate the effectiveness of the proposed metaheuristic methods.</p>

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“…Through the analysis of the above constructed SMISs interdependent network, we find that in the SMISs interdependent network, software failure and hardware failure are two main causes of SMISs failure and there is a cascading relationship between them. 53SMISs are nonlinear systems, and the failure behavior of SMISs cannot be predicted when managers use them to make decisions. 54,55.…”

Section: Smis Interdependent Networkmentioning

confidence: 99%

Chaotic neural network model for SMISs reliability prediction based on interdependent network SMISs reliability prediction by chaotic neural network

Zhu

Zhu-ping

Sun

et al. 2020

Quality & Reliability Eng

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With the development of industrial Internet, smart manufacturing information systems (SMISs) are faced with more uncertainties, dynamics, and complexity. These problems bring more challenges to the reliability operation of SMISs. To solve the above problem, a prediction model based on phase space reconstruction, chaos analysis, and back propagation (BP) neural network is proposed to predict SMISs reliability. First, we decompose failure data series into some subdata series components with strong regularity by using C‐C algorithm and Cao algorithm. On this basis, we use the maximum Lyapunov index to identify chaotic characteristics of failure data series. And then, we establish BP neural network prediction model by using reconstructing failure data to predict SMISs failure behaviors. Finally, we use two groups of failure data series to verify the effectiveness of chaotic BP neural network model, and the experiment results verify that chaotic BP neural network model has more accurate prediction results compared with BP network, support vector machine, long short term memory networks (LSTM), and autoregressive model (AR). LEAD PARAGRAPH SMISs are open and complex systems, human error and external environment cause the uncertainty of reliability. The threat of the external environment mainly comes from malicious attacks and the threat of the human error mainly comes from the wrong operation of the operator. Human error and external environment often cause frequent failures of software and hardware of the system or physical failures of devices. As an open complex system, the reliability operation of SMISs is very important for manufacturing enterprises. However, in the daily use of SMISs, the most common failures are time failure caused by human error and external environment. Therefore, it is very important to study the time failure of SMISs. Main points of this paper: (1) SMISs are complex and open systems, so we establish an interdependent network based on the characteristics of SMISs, and use the cascade effect of the complex network to point out that when SMISs fail, the system will easily fall into failure. (2) The phase space reconstruction method is used to restore the real data characteristics of failure data. (3) By using the reconstructed data and the neural network, the failure behavior can be predicted accurately. Compared with other popular prediction methods, it is found that general machine learning methods cannot predict data with chaotic characteristics. The research results of this paper find that when SMISs fail, the failure behavior can easily lead SMISs into chaos through the propagation of interdependent network. Therefore, when future scholars conduct fault analysis on SMISs, they should consider the chaos of the data, otherwise the systems fault analysis and diagnosis cannot be carried out accurately.

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Improving the Survivability of Clustered Interdependent Networks by Restructuring Dependencies

Cited by 8 publications

References 24 publications

Robust Design of Interdependent Networks Considering Intra-Network Support Flow

Robust Design of Interdependent Networks Considering Intra-Network Support Flow

Robust Design of Interdependent Networks Considering Intra-Network Support Flow

Chaotic neural network model for SMISs reliability prediction based on interdependent network SMISs reliability prediction by chaotic neural network

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