The Experts below are selected from a list of 9774 Experts worldwide ranked by ideXlab platform

Chao Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Resilience Assessment of Interdependent Infrastructure Systems: A Case Study Based on Different Response Strategies
    Sustainability, 2019
    Co-Authors: Jingjing Kong, Slobodan P. Simonovic, Chao Zhang
    Abstract:

    Resilient Infrastructure Systems are essential for continuous and reliable functioning of social and economic Systems. Taking advantage of network theory, this paper models street network, water supply network, power grid and information Infrastructure network as layers that are integrated into a multilayer network. The Infrastructure interdependencies are described using five basic dependence patterns of fundamental network elements. Definitions of dynamic cascading failures and recovery mechanisms of Infrastructure Systems are also established. The main contribution of the paper is a new Infrastructure network resilience measure capable of addressing complex Infrastructure System, as well as network component (layer) interdependences. The new measure is based on Infrastructure network performance, proactive absorptive capacity and reactive restorative capacity, with three resilience features of network—robustness, resourcefulness, and rapidity. The quantitative resilience measure using dynamic space-time simulation model is illustrated with a multilayer Infrastructure network numerical test, including different response strategies to floods of different scale. The results demonstrate that the resilience measure provides an evaluation method of various protection and restoration strategies that will optimize the performance of interdependent Infrastructure System. The sector-specific decisions could not always lead to optimal System solutions, and Systems approach offers significant benefits for increasing Infrastructure System resilience. This study can assist municipal decision makers in (i) better understanding the effects of different response strategies on the resilience of interdependent Infrastructure System, and (ii) deciding which strategy should be adopted under different types of disasters.

  • Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System
    Risk analysis : an official publication of the Society for Risk Analysis, 2018
    Co-Authors: Jingjing Kong, Slobodan P. Simonovic, Chao Zhang
    Abstract:

    Coupled Infrastructure Systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the Infrastructure System resilience. With a case study of the Greater Toronto Area energy System (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent Infrastructure System is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex Infrastructure interdependencies as spatiotemporal impacts on Infrastructure Systems in order to assess the dynamic System resilience. The results confirm that the effects of sequential hazards on resilience of Infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and Infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of Infrastructure and hazard and it can contribute to the improvement of Infrastructure planning, design, and maintenance decision making.

  • Modeling joint restoration strategies for interdependent Infrastructure Systems.
    PloS one, 2018
    Co-Authors: Chao Zhang, Jingjing Kong, Slobodan P. Simonovic
    Abstract:

    Life in the modern world depends on multiple critical services provided by Infrastructure Systems which are interdependent at multiple levels. To effectively respond to Infrastructure failures, this paper proposes a model for developing optimal joint restoration strategy for interdependent Infrastructure Systems following a disruptive event. First, models for (i) describing structure of interdependent Infrastructure System and (ii) their interaction process, are presented. Both models are considering the failure types, Infrastructure operating rules and interdependencies among Systems. Second, an optimization model for determining an optimal joint restoration strategy at Infrastructure component level by minimizing the economic loss from the Infrastructure failures, is proposed. The utility of the model is illustrated using a case study of electric-water Systems. Results show that a small number of failed Infrastructure components can trigger high level failures in interdependent Systems; the optimal joint restoration strategy varies with failure occurrence time. The proposed models can help decision makers to understand the mechanisms of Infrastructure interactions and search for optimal joint restoration strategy, which can significantly enhance safety of Infrastructure Systems.

  • Restoration resource allocation model for enhancing resilience of interdependent Infrastructure Systems
    Safety Science, 2018
    Co-Authors: Chao Zhang, Jingjing Kong, Slobodan P. Simonovic
    Abstract:

    Abstract Enhancing the resilience of Infrastructure Systems is critical to the sustainability of the society against multiple disruptive events. This paper develops an approach for allocating restoration resources to enhance resilience of interdependent Infrastructure Systems. According to Inoperability Input–Output Model, a resilience metric for Infrastructure Systems is developed, in which the performance loss of Infrastructure Systems resulting from a disruptive event is measured in economic loss and inoperability. Model for determining the optimal Infrastructure restoration resources allocation is proposed with the objective of maximizing resilience. Infrastructure interdependence is modeled by the Dynamic Inoperability Input-Output Model (DIIM), which is an accepted economic model for describing the interconnected relationship of industry sectors. To investigate the utility of the restoration resource allocation model, numerical analysis is conducted with an example derived from the data provided by the US Bureau of Economic Analysis. The results show that: (1) the optimal restoration resource allocation varies with the resource budget; (2) for a specific disruptive event, there exists an optimal resource budget which can minimize the sum of restoration cost and the performance loss of Infrastructure System; and (3) the significance of factors such as initial inoperability of Infrastructure Systems on the optimal allocation. The proposed model can assist the decision makers in (i) better understand the effects of resource allocation, and (ii) deciding which allocation strategies should be used following a disruptive event.

Dejun Yang - One of the best experts on this subject based on the ideXlab platform.

  • Smart grid - The new and improved power grid: A survey
    IEEE Communications Surveys and Tutorials, 2012
    Co-Authors: Xi Fang, Satyajayant Misra, Guoliang Xue, Dejun Yang
    Abstract:

    The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major Systems, namely the smart Infrastructure System, the smart management System, and the smart protection System. We also propose possible future directions in each System. colorred{Specifically, for the smart Infrastructure System, we explore the smart energy subSystem, the smart information subSystem, and the smart communication subSystem.} For the smart management System, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection System, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

Poonam Kankariya - One of the best experts on this subject based on the ideXlab platform.

  • secure5g a deep learning framework towards a secure network slicing in 5g and beyond
    2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020
    Co-Authors: Anurag Thantharate, Rahul Arun Paropkari, Vijay Walunj, Cory Beard, Poonam Kankariya
    Abstract:

    Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide an end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc.). Maintaining isolation of resources, traffic flow, and network functions between the slices is critical in protecting the network Infrastructure System from Distributed Denial of Service (DDoS) attack. The 5G network demands and new feature sets to support ever-growing and complex business requirements have made existing approaches to network security inadequate. In this paper, we have developed a Neural Network based ‘Secure5G’ Network Slicing model to proactively detect and eliminate threats based on incoming connections before they infest the 5G core network. ‘Secure5G’ is a resilient model that quarantines the threats ensuring end-to-end security from device(s) to the core network, and to any of the external networks. Our designed model will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single Infrastructure with high security and reliability.

  • CCWC - Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond
    2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020
    Co-Authors: Anurag Thantharate, Rahul Arun Paropkari, Vijay Walunj, Cory Beard, Poonam Kankariya
    Abstract:

    Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide an end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc.). Maintaining isolation of resources, traffic flow, and network functions between the slices is critical in protecting the network Infrastructure System from Distributed Denial of Service (DDoS) attack. The 5G network demands and new feature sets to support ever-growing and complex business requirements have made existing approaches to network security inadequate. In this paper, we have developed a Neural Network based ‘Secure5G’ Network Slicing model to proactively detect and eliminate threats based on incoming connections before they infest the 5G core network. ‘Secure5G’ is a resilient model that quarantines the threats ensuring end-to-end security from device(s) to the core network, and to any of the external networks. Our designed model will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single Infrastructure with high security and reliability.

Jingjing Kong - One of the best experts on this subject based on the ideXlab platform.

  • Resilience Assessment of Interdependent Infrastructure Systems: A Case Study Based on Different Response Strategies
    Sustainability, 2019
    Co-Authors: Jingjing Kong, Slobodan P. Simonovic, Chao Zhang
    Abstract:

    Resilient Infrastructure Systems are essential for continuous and reliable functioning of social and economic Systems. Taking advantage of network theory, this paper models street network, water supply network, power grid and information Infrastructure network as layers that are integrated into a multilayer network. The Infrastructure interdependencies are described using five basic dependence patterns of fundamental network elements. Definitions of dynamic cascading failures and recovery mechanisms of Infrastructure Systems are also established. The main contribution of the paper is a new Infrastructure network resilience measure capable of addressing complex Infrastructure System, as well as network component (layer) interdependences. The new measure is based on Infrastructure network performance, proactive absorptive capacity and reactive restorative capacity, with three resilience features of network—robustness, resourcefulness, and rapidity. The quantitative resilience measure using dynamic space-time simulation model is illustrated with a multilayer Infrastructure network numerical test, including different response strategies to floods of different scale. The results demonstrate that the resilience measure provides an evaluation method of various protection and restoration strategies that will optimize the performance of interdependent Infrastructure System. The sector-specific decisions could not always lead to optimal System solutions, and Systems approach offers significant benefits for increasing Infrastructure System resilience. This study can assist municipal decision makers in (i) better understanding the effects of different response strategies on the resilience of interdependent Infrastructure System, and (ii) deciding which strategy should be adopted under different types of disasters.

  • Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System
    Risk analysis : an official publication of the Society for Risk Analysis, 2018
    Co-Authors: Jingjing Kong, Slobodan P. Simonovic, Chao Zhang
    Abstract:

    Coupled Infrastructure Systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the Infrastructure System resilience. With a case study of the Greater Toronto Area energy System (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent Infrastructure System is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex Infrastructure interdependencies as spatiotemporal impacts on Infrastructure Systems in order to assess the dynamic System resilience. The results confirm that the effects of sequential hazards on resilience of Infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and Infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of Infrastructure and hazard and it can contribute to the improvement of Infrastructure planning, design, and maintenance decision making.

  • Modeling joint restoration strategies for interdependent Infrastructure Systems.
    PloS one, 2018
    Co-Authors: Chao Zhang, Jingjing Kong, Slobodan P. Simonovic
    Abstract:

    Life in the modern world depends on multiple critical services provided by Infrastructure Systems which are interdependent at multiple levels. To effectively respond to Infrastructure failures, this paper proposes a model for developing optimal joint restoration strategy for interdependent Infrastructure Systems following a disruptive event. First, models for (i) describing structure of interdependent Infrastructure System and (ii) their interaction process, are presented. Both models are considering the failure types, Infrastructure operating rules and interdependencies among Systems. Second, an optimization model for determining an optimal joint restoration strategy at Infrastructure component level by minimizing the economic loss from the Infrastructure failures, is proposed. The utility of the model is illustrated using a case study of electric-water Systems. Results show that a small number of failed Infrastructure components can trigger high level failures in interdependent Systems; the optimal joint restoration strategy varies with failure occurrence time. The proposed models can help decision makers to understand the mechanisms of Infrastructure interactions and search for optimal joint restoration strategy, which can significantly enhance safety of Infrastructure Systems.

  • Restoration resource allocation model for enhancing resilience of interdependent Infrastructure Systems
    Safety Science, 2018
    Co-Authors: Chao Zhang, Jingjing Kong, Slobodan P. Simonovic
    Abstract:

    Abstract Enhancing the resilience of Infrastructure Systems is critical to the sustainability of the society against multiple disruptive events. This paper develops an approach for allocating restoration resources to enhance resilience of interdependent Infrastructure Systems. According to Inoperability Input–Output Model, a resilience metric for Infrastructure Systems is developed, in which the performance loss of Infrastructure Systems resulting from a disruptive event is measured in economic loss and inoperability. Model for determining the optimal Infrastructure restoration resources allocation is proposed with the objective of maximizing resilience. Infrastructure interdependence is modeled by the Dynamic Inoperability Input-Output Model (DIIM), which is an accepted economic model for describing the interconnected relationship of industry sectors. To investigate the utility of the restoration resource allocation model, numerical analysis is conducted with an example derived from the data provided by the US Bureau of Economic Analysis. The results show that: (1) the optimal restoration resource allocation varies with the resource budget; (2) for a specific disruptive event, there exists an optimal resource budget which can minimize the sum of restoration cost and the performance loss of Infrastructure System; and (3) the significance of factors such as initial inoperability of Infrastructure Systems on the optimal allocation. The proposed model can assist the decision makers in (i) better understand the effects of resource allocation, and (ii) deciding which allocation strategies should be used following a disruptive event.

Xi Fang - One of the best experts on this subject based on the ideXlab platform.

  • Smart grid - The new and improved power grid: A survey
    IEEE Communications Surveys and Tutorials, 2012
    Co-Authors: Xi Fang, Satyajayant Misra, Guoliang Xue, Dejun Yang
    Abstract:

    The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major Systems, namely the smart Infrastructure System, the smart management System, and the smart protection System. We also propose possible future directions in each System. colorred{Specifically, for the smart Infrastructure System, we explore the smart energy subSystem, the smart information subSystem, and the smart communication subSystem.} For the smart management System, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection System, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.