CRISP 2.0 Type 1: Collaborative Research: Distributed Edge Computing to Improve Resilience of Interdependent Systems

CRISP 2.0 类型 1：协作研究：分布式边缘计算以提高相互依赖系统的弹性

• 批准号: 1832688
• 负责人: Andrew Liu
• 金额: $ 35.21万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1832688&HistoricalAwards=false
• 关键词: CRISP; 2.0; Type; Collaborative; Research

This Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) research advances the body of knowledge on interdependent infrastructure resilience of systems through utilizing distributed assets to minimize cascading failures under extreme events. It is hypothesized that the domino effect of service disruptions is rooted in the vulnerability of the backbone-versus-edge relationship among the systems. For example, a backbone component in one system, such as natural gas-fueled power plants, is only at the end of the supply chain of natural gas (termed as the edge). Consequently, a backbone failure in one system (such as natural gas pipeline outage) can create the domino effect of failures through the entire interdependent systems. One way to alleviate this backbone-vs-edge tension is to bring assets to the edge (referred to as distributed resources), hence releasing the reliance of one infrastructure system on the others. This research will establish a new framework to effectively coordinate among the distributed resources, without requiring centralized coordination. Such a framework will be tested under various hazards including urban droughts, hurricanes and earthquakes. In addition, economic benefits of the added resilience will be quantified to help policy makers with more efficient solutions for improving resilience without sacrificing economic growth. The research will be widely disseminated to scientific communities and public via publishing in scientific outlets as well as leveraging press releases and media tools. Moreover, this research-integrated program and commitment to enhanced diversity promises to inspire underrepresented groups in STEM, and train the next generation of interdisciplinary scholars.To effectively control distributed resources across multiple interdependent systems, a novel distributed optimization algorithm will be established. Most of the existing distributed optimization algorithms cannot deal with complicated (and possibly non-convex) network constraints. To bridge this knowledge gap, there is an algorithm which leverages successive convex approximation, coupled with suitably designed message passing protocols, to allow an optimization problem to be solved in a distributed manner at a large number of computational nodes (i.e., the distributed resources), connected by a network with arbitrary topology and time-varying links. This is particularly useful in modeling outages in physical or communication networks, such as electricity network interruptions. To quantify the benefits of the distributed assets under extreme events, a multi-dimensional resilience quantification framework will be developed to simultaneously characterize multiple performance metrics of systems as opposed to measuring a single performance metric which is the prevalent approach today.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项关键弹性相互依赖基础设施系统和流程 (CRISP) 研究通过利用分布式资产最大限度地减少极端事件下的级联故障，推进了系统相互依赖基础设施弹性的知识体系。据推测，服务中断的多米诺骨牌效应源于系统之间主干与边缘关系的脆弱性。 例如，一个系统中的骨干组件（例如天然气发电厂）仅位于天然气供应链的末端（称为边缘）。因此，一个系统中的主干故障（例如天然气管道中断）可能会在整个相互依赖的系统中产生故障的多米诺骨牌效应。缓解这种骨干与边缘紧张关系的一种方法是将资产带到边缘（称为分布式资源），从而释放一个基础设施系统对其他基础设施系统的依赖。这项研究将建立一个新的框架来有效地协调分布式资源，而不需要集中协调。这样的框架将在包括城市干旱、飓风和地震在内的各种灾害下进行测试。此外，增强韧性带来的经济效益将被量化，以帮助政策制定者提供更有效的解决方案，在不牺牲经济增长的情况下提高韧性。该研究将通过在科学媒体上发表以及利用新闻稿和媒体工具向科学界和公众广泛传播。此外，这一研究整合计划和对增强多样性的承诺有望激励 STEM 中代表性不足的群体，并培养下一代跨学科学者。 为了有效地控制多个相互依赖的系统中的分布式资源，将建立一种新颖的分布式优化算法。大多数现有的分布式优化算法无法处理复杂的（并且可能是非凸的）网络约束。为了弥补这一知识差距，有一种算法利用逐次凸逼近，结合适当设计的消息传递协议，允许在大量计算节点（即分布式资源）上以分布式方式解决优化问题。 ，由具有任意拓扑和时变链路的网络连接。这对于对物理或通信网络的中断（例如电力网络中断）进行建模特别有用。为了量化极端事件下分布式资产的收益，将开发多维弹性量化框架，以同时表征系统的多个性能指标，而不是当今流行的测量单一性能指标。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Finite-Bit Quantization for Distributed Algorithms With Linear Convergence

• DOI: 10.1109/tit.2022.3176253
• 发表时间: 2021-07
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Chang-Shen Lee;Nicolò Michelusi;G. Scutari

ASY-SONATA: Achieving Linear Convergence in Distributed Asynchronous Multiagent Optimization

• DOI: 10.1109/allerton.2018.8636055
• 发表时间: 2018-10
• 期刊: 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
• 作者: Ye Tian;Ying Sun;G. Scutari

Critical Time, Space, and Decision‐Making Agent Considerations in Human‐Centered Interdisciplinary Hurricane‐Related Research

关键时间、空间和决策——以人为中心的跨学科飓风中的代理人考虑因素——相关研究

• DOI: 10.1111/risa.13380
• 发表时间: 2019
• 期刊: Risk Analysis
• 影响因子: 3.8
• 作者: Murray‐Tuite, Pamela;Ge, Y. Gurt;Zobel, Christopher;Nateghi, Roshanak;Wang, Haizhong
• 通讯作者: Wang, Haizhong

Asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions

• DOI: 10.1109/tac.2020.3033490
• 发表时间: 2020-10
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Loris Cannelli;F. Facchinei;G. Scutari;V. Kungurtsev

Analyzing the climate sensitivity of the coupled water-electricity demand nexus in the Midwestern United States

• DOI: 10.1016/j.apenergy.2019.113466
• 发表时间: 2019-10-15
• 期刊: APPLIED ENERGY
• 影响因子: 11.2
• 作者: Obringer, R.;Kumar, R.;Nateghi, R.
• 通讯作者: Nateghi, R.