CPS: Medium: Collaborative Research: Mitigation strategies for enhancing performance while maintaining viability in cyber-physical systems

CPS：中：协作研究：在保持网络物理系统可行性的同时提高性能的缓解策略

• 批准号: 1932530
• 负责人: Bruno Sinopoli
• 金额: $ 80万
• 依托单位: Washington 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: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932530&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Mitigation

Complex cyber-physical systems (CPS) that operate in dynamic and uncertain environments will inevitably encounter unanticipated situations during their operation. Examples range from naturally occurring faults in both the cyber and physical components to attacks launched by malicious entities with the purpose of disrupting normal operations. As infrastructures, e.g. energy, transportation, industrial systems and built environments, are getting smarter, the chance of a fault or attack increases. When this happens, it is essential that system behavior remains viable, i.e., it does not violate pre-specified operating constraints on run-time behavior. Preserving safety, for instance, is of paramount importance to avoid damage and possible loss of life. This project will develop strategies for mitigating the effects of such unanticipated situations, that seek to optimize for performance (measured by multiple metrics such as cost, efficiency, accuracy, etc.) without compromising viability. The emphasis will be on the automotive application domain, given the upcoming revolution brought by innovations such as vehicle-to-vehicle (V2V), vehicle to infrastructure (V2I) communication and autonomous driving, and because of the safety-criticality of the transportation infrastructure. To ground our research on relevant problems, we will engage industrial partners. The outcomes of the project will be validated upon test scenarios drawn from the automotive industry. Fundamental issues arising when safety-critical CPS operate in uncertain environments will be addressed, with the objective of obtaining a better understanding of, and developing optimal or near-optimal strategies for dealing with, emergent problems that arise from the interaction of resource-allocation and control strategies in such systems. One of the novelties of the technical approach adopted in this project is to closely integrate three different CPS perspectives ? control theory, automotive & aerospace application domain-knowledge, and real-time resource management & scheduling ? in order to develop run-time mitigation strategies for complex CPS's operating in dynamic and uncertain environments, and exposed to a variety of faults. Such an integrated approach will allow for the identification of emergent problems that arise from the interaction of resource-allocation and control algorithms, that may otherwise remain undiscovered if the control and resource-allocation aspects were considered separately.The general design-time and run-time tools for creating resilient CPSs will be guided by the implementation and evaluation of the research in simulation and on laboratory test-beds upon three applications from the automotive domain: fault resilience for variable-valve internal combustion engines; fail-safe energy management for hybrid-electric vehicles; and robust sensor management for autonomous vehicles.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.

运行在动态和不确定环境中的复杂网络物理系统在运行过程中不可避免地会遇到意想不到的情况。例如，从网络和物理组件中自然发生的故障到恶意实体发起的旨在扰乱正常运营的攻击。随着能源、交通、工业系统和建筑环境等基础设施变得越来越智能，发生故障或攻击的可能性也增加了。当这种情况发生时，至关重要的是系统行为保持可行，即它不违反对运行时行为的预先指定的操作约束。例如，保护安全对于避免损坏和可能的生命损失至关重要。该项目将制定策略，以减轻此类意外情况的影响，寻求优化性能(通过成本、效率、准确性等多个指标进行衡量)。而不会影响生存能力。重点将放在汽车应用领域，因为车辆到车辆(V2V)、车辆到基础设施(V2I)通信和自动驾驶等创新带来的即将到来的革命，以及交通基础设施的安全关键。为了使我们的研究立足于相关问题，我们将邀请行业合作伙伴参与。该项目的结果将根据来自汽车行业的测试场景进行验证。将解决安全关键CP在不确定环境中运行时出现的基本问题，目的是更好地了解并制定最优或接近最优的战略，以处理此类系统中资源分配和控制战略相互作用所产生的紧急问题。这个项目采用的技术方法的新颖性之一是紧密结合了三个不同的CPS视角？控制理论、汽车和航空航天应用领域的知识，以及实时资源管理和调度？为了制定复杂CPS在动态和不确定环境中运行时的缓解策略，并暴露在各种故障中。这种综合方法将能够识别资源分配和控制算法相互作用产生的紧急问题，否则，如果单独考虑控制和资源分配方面，这些问题可能仍未被发现。创建弹性CPSS的一般设计时和运行时工具将以模拟和实验室试验台上的研究的实施和评估为指导，这些研究来自汽车领域的三个应用：可变气门内燃机的故障弹性；混合动力汽车的故障安全能量管理；这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Scheduling IDK classifiers with arbitrary dependences to minimize the expected time to successful classification

调度具有任意依赖性的 IDK 分类器，以最大限度地缩短成功分类的预期时间

• DOI: 10.1007/s11241-023-09395-0
• 发表时间: 2023
• 期刊: Real-Time Systems
• 影响因子: 1.3
• 作者: Abdelzaher, Tarek;Agrawal, Kunal;Baruah, Sanjoy;Burns, Alan;Davis, Robert I.;Guo, Zhishan;Hu, Yigong
• 通讯作者: Hu, Yigong

Feasibility analysis for HPC-DAG tasks

HPC-DAG任务的可行性分析

• DOI: 10.1007/s11241-022-09380-z
• 发表时间: 2022
• 期刊: Real-Time Systems
• 影响因子: 1.3
• 作者: Baruah, Sanjoy

Optimally ordering IDK classifiers subject to deadlines

根据截止日期优化订购 IDK 分类器

• DOI: 10.1007/s11241-022-09383-w
• 发表时间: 2022
• 期刊: Real-Time Systems
• 影响因子: 1.3
• 作者: Baruah, Sanjoy;Burns, Alan;Davis, Robert I.;Wu, Yue
• 通讯作者: Wu, Yue

Algorithms for implementing elastic tasks on multiprocessor platforms: a comparative evaluation

在多处理器平台上实现弹性任务的算法：比较评估

• DOI: 10.1007/s11241-020-09358-9
• 发表时间: 2021
• 期刊: Real-Time Systems
• 影响因子: 1.3
• 作者: Orr, James;Baruah, Sanjoy
• 通讯作者: Baruah, Sanjoy

Optimal Synthesis of Robust IDK Classifier Cascades

鲁棒 IDK 分类器级联的优化合成

• DOI: 10.1145/3609129
• 发表时间: 2023
• 期刊: ACM Transactions on Embedded Computing Systems
• 影响因子: 2
• 作者: Baruah, Sanjoy;Burns, Alan;Davis, Robert Ian
• 通讯作者: Davis, Robert Ian