DDDAS-TMRP: A Generic Multi-scale Modeling Framework for Reactive Observing Systems

观测系统通过对真实的世界进行测量并收集相应的测量结果，以探测和跟踪感兴趣的现象，从而促进科学研究。在这个建议中，我们专注于一类观测系统，这是（1）嵌入到环境中，（2）由固定和移动的传感器，和（3）通过重新配置系统和调整哪些观察收集下一个，这些被称为反应式观测系统（ROS）。ROS的目标是帮助科学家通过固定和移动的单元采集的有用样本来验证或证伪假设，以及自主分析数据以发现有趣的趋势或警报条件。在资源管理、环境保护和公共卫生等领域的广泛关键环境监测目标都需要分布式观测系统。该项目将在海洋生物学应用的背景下探索ROS，其中系统监测，例如，水温和光照以及水体中微生物和藻类的浓度。该系统使用固定和移动的传感器的混合网络，通过有线和无线链路进行通信，近实时地收集感兴趣信息的细粒度测量。使用这种系统的一个例子是快速识别微生物，以预测藻类大量繁殖的开始。这种繁荣可能会带来毁灭性的经济后果。 目前的技术无法对所有可能的相关数据进行采样。 因此，需要开发用于优化和控制要在任何给定时间采集的样本集的方法，同时考虑应用的目标和系统资源约束。为了支持这样的优化和控制过程，框架的重要部分必须致力于数据模型的开发及其自动验证或调整。作为验证和适应过程的一部分，框架还必须包括用于定位感兴趣的数据的分布式支持机制。在该项目中追求的方法包括ROS的多尺度建模框架，该框架允许应用程序基于收集的数据构建不同时空范围的相互关联的模型。在模型的指导下，系统的反应元素预测可能发现有趣数据和现象的位置。在构建模型的过程中，系统主动寻找最有用的数据，以改进模型和现象检测和跟踪。在反馈周期中，这种数据采集由先前的、可能不太精确的模型指导。因此，待开发的系统（AMBROsia）能够以尊重系统资源约束的方式实现测量的最佳收集，还提高了现象检测和跟踪的总体保真度。 这样一个系统将通过促进科学假设的检验来帮助科学研究。它将及时预测取样需求，并提供动态现象的跟踪信息。总的来说，AMBROSia将有助于观察、检测和跟踪以前只能部分（或根本不能）观察和/或理解的科学现象。

Observing systems facilitate scientific studies by instrumenting the real world and collecting corresponding measurements, with the aim of detecting and tracking phenomena of interest. In this proposal, we focus on a class of observing systems which are (1) embedded into the environment, (2) consist of stationary and mobile sensors, and (3) react to collected observations by reconfiguring the system and adapting which observations are collected next, these are referred to as Reactive Observing Systems (ROS). The goal of ROS is to help scientists verify or falsify hypotheses with useful samples taken by the stationary and mobile units, as well as to analyze data autonomously to discover interesting trends or alarming conditions.A wide range of critical environmental monitoring objectives in resource management, environmental protection, and public health all require distributed observing systems. This project will explore ROS in the context of a marine biology application, where the system monitors, e.g., water temperature and light as well as concentrations of micro-organisms and algae in a body of water. Using a hybrid network of stationary and mobile sensors, communicating both via wired and wireless links, the system collects fine-grained measurements of interesting information in near real-time. An example of the use of such a system is the rapid identification of micro-organisms to predict the onset of algal blooms. Such blooms can have devastating economic consequences. Current technology precludes sampling all possibly relevant data. Therefore there is need to develop approaches for optimizing and controlling the set of samples to be taken at any given time, taking into consideration the application's objectives and system resource constraints. To support such an optimization and control process, a significant part of the framework must be dedicated to the development of models of data, and their automatic validation or adaptation. As part of the validation and adaptation process, the framework must also include a distributed support mechanism for locating data of interest. The methods to be pursued in the project include a multi-scale modeling framework for ROS, that allows applications to construct inter-related models of varying spatio-temporal scope based on collected data. Guided by the models, the reactive elements of the system predict where interesting data and phenomena are likely to be found. In the process of constructing models, the system actively seeks most useful data to improve both, the models and phenomenon detection and tracking. In a feedback cycle, this data acquisition is guided by previous, perhaps less precise, models. Thus, the system to be developed (AMBROsia) enables optimal collection of measurements in a manner that respects system resource constraints, yet improves the overall fidelity of phenomenon detection and tracking. Such a system will aid scientific research by facilitating the testing of scientific hypothesis. It will provide timely predictions of sampling needs, and tracking information for dynamic phenomena. Overall, AMBROSia will facilitate observation, detection, and tracking of scientific phenomena that were previous only partially (or not at all) observable and/or understood.