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Sparse Sensing, Actuation, and Communication in Complex Networks

复杂网络中的稀疏传感、驱动和通信

基本信息

批准号：
2121121
负责人：
Milad Siami
金额：
$ 30万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121121&HistoricalAwards=false
关键词：
Sparse Sensing Actuation Communication Complex

项目摘要

Social networks that connect people, smart power grids, pandemic influenza mitigation, and flying drone display are all examples of large-scale complex networks. Due to the various worldwide applications of these networks, achieving efficient and robust control and monitoring of such networks is crucial, especially using limited resources. We seek to accelerate the development of novel technologies and advanced algorithms for complex dynamical networks using recent advances in theoretical computer science, graph theory, and machine learning. The results of this project will provide a rich set of building blocks to find a sparse yet important subset of available sensors and actuators in the complex network to enable precise control and high-resolution monitoring of the entire network. As an integral part of this research program, we also propose an educational plan involving K-12, undergraduate, and graduate-level education. The outreach element will improve the pre-college students' awareness of the impact and attractiveness of both research and engineering careers. Moreover, we aim to leverage the resources at Northeastern's University Program in Multicultural Engineering to broaden students' professional skills, find valuable contacts, and explore one or more career paths. We also expect to collect the results of this research in a special topic graduate-level course documenting the theoretical underpinnings and algorithmic implementations of sparse interaction in complex networks.Given the increasingly large-scale nature of complex networks, it is crucial to rapidly estimate and control the state of the overall network in a distributed fashion with provable performance guarantees, while using a minimum amount of actuations, observations, and communications at each node and link over time. The main goal of this proposal is to generalize collaborative subset selection methods, and adaptive samplings in machine learning to (i) reduce network complexity and data overload by sparse scheduling of available Sensor/Actuator/Coupling measurements in complex networks, (ii) deal with structured uncertainties, missing information, and corruptions on the update dynamics of each agent or communication link, and (iii) handle a large class of controllability and observability performance measures that are not supermodular or submodular.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.

连接人们的社交网络、智能电网、大流行流感缓解和无人机飞行展示都是大规模复杂网络的例子。由于这些网络在世界范围内的各种应用，实现对这些网络的有效和可靠的控制和监测是至关重要的，尤其是使用有限的资源。我们寻求利用理论计算机科学、图论和机器学习的最新进展来加速复杂动态网络的新技术和高级算法的开发。该项目的结果将提供丰富的构建块集，以在复杂网络中找到稀疏但重要的可用传感器和执行器子集，以实现对整个网络的精确控制和高分辨率监控。作为这项研究计划的组成部分，我们还提出了一项涉及K-12、本科生和研究生教育的教育计划。外展元素将提高大学预科学生对研究和工程职业的影响和吸引力的认识。此外，我们的目标是利用东北大学多元文化工程项目的资源来拓宽学生的专业技能，找到有价值的联系人，并探索一条或多条职业道路。我们还希望在研究生级别的专题课程中收集这项研究的结果，该课程记录了复杂网络中稀疏交互的理论基础和算法实现。鉴于复杂网络的规模越来越大，关键是以分布式的方式快速估计和控制整个网络的状态，并提供可证明的性能保证，同时使用随时间推移在每个节点和链路上使用最少的激励、观察和通信。这项建议的主要目标是推广机器学习中的协作性子集选择方法和自适应采样，以(I)通过稀疏调度复杂网络中可用的传感器/执行器/耦合测量来降低网络复杂性和数据过载，(Ii)处理每个代理或通信链路更新动态上的结构化不确定性、丢失信息和损坏，以及(Iii)处理大类非超模块或子模块的可控性和可观测性性能衡量标准。该奖项反映了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。