CPS: Small: Recovery Algorithms for Dynamic Infrastructure Networks

CPS：小型：动态基础设施网络的恢复算法

• 批准号: 1739505
• 负责人: Hamsa Balakrishnan
• 金额: $ 44.98万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-11-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739505&HistoricalAwards=false
• 关键词: CPS; Small; Recovery; Algorithms; Dynamic

Most critical infrastructures have evolved into complex systems comprising large numbers of interacting elements. These interactions result in the spread of disruptions, such as delays, from one part of the system to another, and even from one infrastructure to another. Effective tools for the analysis and control of real-world infrastructures need to account for the underlying dynamics.The key insight in this research is that by learning data-driven models of infrastructure networks, and using these models to determine dynamics-aware recovery algorithms, we can greatly improve the resilience of critical infrastructure networks. We propose to address these challenges by:1. Learning and validating scalable representations of real systems from data. By considering continuous states, and by modeling the time-varying nature of connectivity as switching between network topologies, we propose to obtain a class of switched linear system models. Multilayer network models will be developed to account for airline networks, and multimodal systems.2. Characterizing resilience, both for the system as a whole, and in terms of individual nodes (e.g., susceptibility to network delays). The metrics to evaluate resilience will encompass both steady-state and transient behavior.3. Using the identified models to design optimal control algorithms that can enable recovery from disruptions, taking into account network dynamics, the uncertainty in operating environments, and the costs of decisions to restore service at various levels, at various times.The results of the research will be validated using operational data, thereby yielding a set of tools for system diagnostics, analysis, and recovery. Improving and maintaining critical infrastructures are among the grand challenges identified by the National Academy of Engineering. The proposed research will develop techniques grounded in network science, machine learning, and systems and control theory in order to effectively design and operate infrastructures. The development of common frameworks and abstractions for these infrastructures will enable the study of their interdependencies. With the rapid growth of intelligent infrastructures, the proposed research will benefit society, and also help attract and train the next generation of engineering professionals.

大多数关键基础设施已发展成为由大量相互作用的要素组成的复杂系统。这些交互导致中断的传播，例如延迟，从系统的一个部分到另一个部分，甚至从一个基础设施到另一个基础设施。有效的工具来分析和控制现实世界的基础设施需要考虑到底层的动态性，在这项研究中的关键洞察力是，通过学习基础设施网络的数据驱动模型，并使用这些模型来确定动态感知的恢复算法，我们可以大大提高关键基础设施网络的弹性。我们建议通过以下方式应对这些挑战：1.从数据中学习和验证真实的系统的可扩展表示。通过考虑连续状态，并通过建模的时变性质的连接网络拓扑结构之间的切换，我们建议得到一类切换线性系统模型。多层网络模型将被开发来解释航线网络和多模式系统。描述整个系统和单个节点的弹性特征（例如，对网络延迟的敏感性）。评估弹性的指标将包括稳态和瞬态行为。使用确定的模型来设计最优控制算法，可以从中断中恢复，考虑到网络动态，在操作环境中的不确定性，并在不同的时间恢复服务的决策的成本在不同的级别，研究的结果将使用操作数据进行验证，从而产生一套工具，系统诊断，分析和恢复。改善和维护关键基础设施是美国国家工程院确定的重大挑战之一。拟议的研究将开发基于网络科学，机器学习，系统和控制理论的技术，以有效地设计和运营基础设施。这些基础设施的共同框架和抽象的发展将使他们的相互依存关系的研究。随着智能基础设施的快速发展，拟议的研究将造福社会，并有助于吸引和培养下一代工程专业人才。

项目成果

Differentially Private Outlier Detection in Correlated Data

相关数据中的差分隐私异常值检测

• 发表时间: 2021
• 期刊: IEEE Conference on Decision and Control
• 作者: Degue, K. H.;Gopalakrishnan, K.;Li, M.Z.;Balakrishnan, H.
• 通讯作者: Balakrishnan, H.

Differentially Private Outlier Detection in Multivariate Gaussian Signals

多元高斯信号中的差分隐私异常值检测

• DOI: 10.23919/acc50511.2021.9483171
• 发表时间: 2021
• 期刊: 2021 American Control Conference (ACC
• 作者: Degue, Kwassi H.;Gopalakrishnan, Karthik;Li, Max Z.;Balakrishnan, Hamsa;Ny, Jerome Le
• 通讯作者: Ny, Jerome Le

Sign-stability of Positive Markov Jump Linear Systems

• DOI: 10.1016/j.automatica.2019.108638
• 发表时间: 2020
• 期刊: Autom.
• 作者: J. Cavalcanti;H. Balakrishnan

Network-centric benchmarking of operational performance in aviation

以网络为中心的航空运营绩效基准测试

• DOI: 10.1016/j.trc.2021.103041
• 发表时间: 2021
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Gopalakrishnan, Karthik;Li, Max Z.;Balakrishnan, Hamsa
• 通讯作者: Balakrishnan, Hamsa

Equity Impacts of the London Congestion Charging Scheme: An Empirical Evaluation Using Synthetic Control Methods

伦敦拥堵收费计划的公平影响：使用综合控制方法的实证评估

• DOI: 10.1177/03611981221138801
• 发表时间: 2023
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Craik, Lauren;Balakrishnan, Hamsa
• 通讯作者: Balakrishnan, Hamsa