Collaborative Research: Selecting Sensors and Actuators for Topologically Evolving Networked Dynamical Systems: Battling Contamination in Water Networks

该项目的目标是动态管理网络系统中的传感器和执行器，以最大限度地减少饮用水网络中的污染。现代基础设施的一个决定性特征是实时传感和驱动设备的普及和丰富。通过支持互联网的设备增加智能社区的集成将实现卓越的系统级基础设施性能和可靠性。电网、供水系统和交通网络共用数十亿个传感器和执行器。尽管传感和驱动设备数量的指数级增长提供了丰富的社会价值，但这些设备的实时管理成为系统利益相关者的一项艰巨任务。传感器和执行器的动态部署，执行器实现最佳动作，传感器有选择地报告时间和空间关键数据，将带来显著的社会经济效益。该项目处理的具体主题是控制配水网络的污染水平和减少电网的能源消耗。该项目的研究目标是创建基本的科学方法，指导网络系统利益相关者在这些系统中不可避免的拓扑进化和不确定性中自适应选择最可靠的传感器和执行器。低频率或高频拓扑演化是网络系统物理变化的自然结果。例如，网络中增加了节点和链路，导致拓扑结构发生变化。先前的相关工作主要集中在大多数线性系统的传感器和执行器的调度或一次性放置问题上。相比之下，本研究除了考虑广泛的控制理论指标外，还研究了随着网络条件的变化自适应选择传感和驱动设备的方法。这种方法除了确保对不确定性的鲁棒性之外，还显著增强了网络系统和基础设施对拓扑变化的弹性。所调查的研究还对无污染配水网络的质量控制产生重要影响，该网络利用穿越管道和水箱的高端移动水传感器，同时每隔几秒钟通过无线通信获取数据。利用水网缓慢的时间尺度，基于半定规划和混合整数规划，开发了最优和次最优在线算法。这些算法利用网络系统固有的稀疏性来获得最佳的去污注入时间和位置，作为执行器，同时从移动水传感器采集数据。这最终可以保证饮用水网络中的污染程度降到最低。

The objective of this project is the dynamic management of sensors and actuators in networked systems with applications to minimizing contamination in drinking water networks. A defining feature of modern infrastructures is the prevalence and abundance of real-time sensing and actuation devices. The increased integration of smart communities through Internet-enabled devices will achieve superior system-level infrastructure performance and reliability. Power grids, water systems, and transportation networks share billions of sensors and actuators amongst them. Although the exponential increase in the number of sensing and actuating devices offers an abundance of societal merits, the real-time management of these devices becomes a daunting task for system stakeholders. The dynamic deployment of sensors and actuators, actuators implementing optimal actions, and sensors selectively reporting time- and space-critical data, will lead to significant socio-economic gains. The specific themes addressed by this project are curbing contamination levels in water distribution networks and reducing energy consumption in power grids. The research goal of this project is to create fundamental scientific methods that guide networked systems stakeholders in the adaptive selection of the most reliable sensors and actuators--amid the inevitable topologically evolution and uncertainty in these systems. The low- or high-frequency topological evolution is a natural consequence of the physical changes in networked systems. For example, the addition of nodes and links in networks causes a change in topology. Related prior work focused on problems of scheduling or one-time placement of sensors and actuators for mostly linear systems. In contrast, this research investigates methods for adaptively selecting sensing and actuating devices as network conditions change, in addition to considering a wide range of control-theoretic metrics. Such an approach significantly enhances the resilience of networked systems and infrastructures to changes in topology, in addition to ensuring robustness against uncertainty. The investigated research also has important impacts on quality control of contamination-free water distribution networks that leverage high-end mobile water sensors traversing pipes and tanks, while acquiring data through wireless communications every few seconds. Exploiting the slow time-scales of water networks, optimal and sub-optimal online algorithms are developed based on semidefinite and mixed-integer programming. These algorithms capitalize on the inherent sparsity of networked systems to obtain the optimal timing and location of decontaminant injections, acting as actuators, while simultaneously sampling data from mobile water sensors. This can ultimately guarantee a minimal level of contamination in drink water networks.