| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 18, 2015 |
| Latest Amendment Date: | August 18, 2015 |
| Award Number: | 1541074 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $499,920.00 |
| Total Awarded Amount to Date: | $499,920.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
225 North Avenue Atlanta GA US 30332-0002 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Special Projects - CNS |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project will create a novel modeling framework to assess and control interdependent critical infrastructure systems (ICIs). Infrastructure systems are critical to the functioning of our society, and the services they deliver form the backbone of the health, safety, and security of our nation. These systems are complex, comprised of many interdependent components. Further, these systems are interdependent, with the performance of one system dependent on the performance of one or more of the others. This leaves ICIs vulnerable to a variety of hazards, both natural and manmade. This project will study how to improve the resilience of these systems, with the recognition that achieving resilience will be a shared responsibility among stakeholders. At the same time, more and more data is becoming available to assess the states of ICIs both under normal conditions and over time.
This project will take a multidisciplinary approach, integrating across engineering, computation, and policy to create a powerful stakeholder-driven framework that models ICIs across scales and utilizes data across sources to evaluate the current status of infrastructures and make predictions on their performance and reliability. The researchers will study three ICIs in particular: transportation, power, and communications infrastructure, applying the framework to the study of these ICIs in two specific communities, one urban and one rural. The framework will be created in conjunction with the development of new processes to achieve stakeholder buy-in and policy adoption to support integration of the new technology with policy. With fast algorithms to solve the models of the framework and real-time (or near-real-time) data collection capabilities, a powerful resilient infrastructure management system that can react, adapt, and even proactively take precautionary actions in anticipation of impending disasters is envisioned.
The results of this project will also be integrated into extensive classroom and educational research activities, training the next generation of scientists, engineers, and policymakers on the importance of critical infrastructure resilience and in the development of new multi-disciplinary methods and tools to achieve resilience.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project has resulted in the creation of a novel modeling framework for interdependent critical infrastructure systems (ICIs). The framework accounts for the complex connections between interdependent infrastructure components both within and across multiple systems. It models ICIs across scales and utilizes data across sources to evaluate the current status of infrastructures and make predictions on their performance and reliability. As part of this project, three generalized, comprehensive infrastructure interdependency types were defined, and rigorous probabilistic modeling of each interdependency type was achieved. This project also resulted in the creation of a flexible data integration framework that is able to take data from across sources for integrated real-time ICI assessment. The project showed the ability to combine data from multiple sources, even when the data is heterogeneous or the sensor sources vary in reliability, to better understand the real-time state of systems. With fast algorithms to solve the models of the framework and real-time (or near-real-time) data collection, the result is a powerful resilient infrastructure management system with comprehensive analysis and quickly updating capabilities.
This project has also resulted in extensive stakeholder, education, and training impacts. Working with ICI stakeholders and policymakers has resulted in the integration of the new technology with policy, including policies to leverage crowdsourced data to improve infrastructure systems and to include interdependencies in infrastructure assessment. The research activities were also integrated into classroom, educational, and professional training activities across the disciplines of the collaborators of the project in engineering, computing, and public policy. This includes classroom training through existing and new courses with cross-disciplinary enrollment in the areas of critical infrastructure risk, reliability, security, and resilience; and the professional development of multiple graduate students, including women in STEM. This project has resulted in the training of the next generation of scientists, engineers, and policymakers on the importance of critical infrastructure resilience and in the development of new multi-disciplinary methods and tools to achieve resilience.
Last Modified: 11/11/2019
Modified by: Iris Tien
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