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的应用，以尽量减少饮用水网络中的污染。现代基础设施的定义特征是实时感测和致动设备的流行和丰富。通过互联网设备增加智能社区的整合，将实现上级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享数十亿个传感器和执行器。尽管传感和致动设备数量的指数增长提供了丰富的社会价值，但这些设备的实时管理对于系统利益相关者来说是一项艰巨的任务。传感器和执行器的动态部署、执行最佳行动的执行器以及有选择地报告时间和空间关键数据的传感器将带来重大的社会经济收益。该项目的具体主题是控制供水网络的污染程度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。低频或高频拓扑演化是网络系统中物理变化的自然结果。例如，在网络中添加节点和链路会导致拓扑结构发生变化。相关的先前工作集中在调度或一次性放置的传感器和执行器的问题，主要是线性系统。相比之下，本研究探讨的方法，自适应选择传感和驱动设备的网络条件的变化，除了考虑范围广泛的控制理论指标。这种方法除了确保对不确定性的鲁棒性之外，还显著增强了联网系统和基础设施对拓扑变化的弹性。调查研究还对无污染配水网络的质量控制产生了重要影响，该网络利用穿过管道和水箱的高端移动的水传感器，同时每隔几秒钟通过无线通信获取数据。利用水网络的慢时间尺度，基于半定规划和混合整数规划提出了最优和次优在线算法。这些算法利用网络系统的固有稀疏性来获得净化剂注入的最佳时间和位置，充当致动器，同时从移动的水传感器采样数据。这最终可以保证饮用水网络中的污染水平最低。

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.