Sparse Sensing, Actuation, and Communication in Complex Networks

连接人们的社交网络、智能电网、大流行性流感缓解和飞行无人机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稳健的控制和监测至关重要，特别是在使用有限资源的情况下。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先进算法的发展。该项目的结果将提供一套丰富的构建模块，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，从而实现对整个网络的精确控制和高分辨率监测。作为这个研究项目的一个组成部分，我们还提出了一个涉及K-12，本科和研究生水平教育的教育计划。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，拓宽学生的专业技能，找到有价值的联系，并探索一条或多条职业道路。我们还希望在一个专门的研究生课程中收集这项研究的结果，记录复杂网络中稀疏交互的理论基础和算法实现。考虑到复杂网络日益大规模的本质，以分布式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，同时在每个节点和链路上使用最少的驱动、观察和通信，这一点至关重要。本提案的主要目标是推广协作子集选择方法，以及机器学习中的自适应采样(i)通过在复杂网络中稀疏调度可用的传感器/执行器/耦合测量来降低网络复杂性和数据过载，（ii）处理结构化不确定性，缺失信息，以及每个代理或通信链路更新动态的破坏。（iii）处理一大类非超模或次模的可控性和可观察性性能度量。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.