CyberSEES: Type 2: Towards Sustainable Aquatic Ecosystems: A New Adaptive Sampling and Data-Enabled Monitoring and Modeling Framework

利用水下机器人、传感器网络、信号处理和生物物理建模方面的进步，该奖项的目标是为监测和理解水生生态系统创造一个新的范例，从而实现水资源的可持续管理。在这个范例中，自主滑翔机器鱼群自适应地对水环境进行采样。所收集的测量数据被用来重建高分辨率的数据场与先进的多维信号处理算法。重建的数据字段沿着数据样本有助于监测水生生态系统，并能够对潜在的生物物理过程进行高保真的机械建模，以进行准确的预测。该奖项的目标包括解决范式构建块之间接口的基本问题，并展示后者的概念证明。具体而言，五个高度综合的研究重点是追求：（1）为机器人开发路径规划和控制算法，以实现信息驱动的节能采样，（2）开发鲁棒的通信协议和有效的网络内参数估计算法，（3）建立基于张量稀疏化的框架，用于使用有限的数据样本进行数据场重建，（4）利用重构的数据场和网络估计的子模型来创建精确的机理模型;（5）评估和展示有害藻类水华监测和预测的集成范式。该奖项预计将产生一个新的，全面的框架，用于监测，理解和管理淡水和海洋环境，并在溢油反应，生态系统监测和饮用水安全等方面有大量应用。该项目为学生，包括代表性不足群体的学生提供跨学科培训机会。机器鱼演示，博物馆展览和教师培训活动提供参与K-12学生，教师和公众，并激起他们对科学和工程的兴趣。除了通过会议报告、出版物和讲习班传播研究成果外，还努力使开发的技术商业化，以促进其实际采用。

Exploiting advances in underwater robotics, sensor networks, signal processing, and biophysical modeling, the goal of this award is to create a novel paradigm for monitoring and understanding aquatic ecosystems and thus enable sustainable management of water resources. In this paradigm, schools of autonomous gliding robotic fish adaptively sample the water environment. The collected measurements are used to reconstruct high-resolution data fields with advanced multidimensional signal processing algorithms. The reconstructed data fields, along with the data samples, facilitate the monitoring of aquatic ecosystems and enable high-fidelity, mechanistic modeling of the underlying biophysical processes for accurate forecast. The objectives of this award include addressing fundamental problems at the interfaces between the building blocks of the paradigm, and demonstrating a proof of concept for the latter. Specifically, five highly integrated research thrusts are pursued: (1) developing path-planning and control algorithms for the robots to realize information-driven, energy-efficient sampling, (2) developing robust communication protocols and effective in-network parameter-estimation algorithms, (3) establishing tensor sparsification-based frameworks for data-field reconstruction using limited data samples, (4) exploiting reconstructed data fields and network-estimated sub-models to create accurate mechanistic models, and (5) evaluating and demonstrating the integrative paradigm in the monitoring and prediction of Harmful Algal Blooms. This award is expected to result in a new, holistic framework for monitoring, understanding, and managing freshwater and marine environments, with a myriad of applications in oil spill response, ecosystem monitoring, and drinking water safety, to name a few. The project provides interdisciplinary training opportunities for students, including those from underrepresented groups. Robotic fish demos, museum exhibits, and teacher-training activities are offered to engage K-12 students, teachers, and the public, and to pique their interest in science and engineering. Besides the dissemination of research findings through conference presentations, publications, and workshops, commercialization of the developed technologies is pursued to facilitate their practical adoption.