CPS: Medium: Collaborative Research: Physical Modeling and Software Synthesis for Self-Reconfigurable Sensors in River Environments

CPS：中：协作研究：河流环境中自重构传感器的物理建模和软件综合

• 批准号: 0931348
• 负责人: Alexandre Bayen
• 金额: $ 28.33万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0931348&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Physical

The objective of this research is the transformation from static sensing into mobile, actuated sensing in dynamic environments, with a focus on sensing in tidally forced rivers. The approach is to develop inverse modeling techniques to sense the environment, coordination algorithms to distribute sensors spatially, and software that uses the sensed environmental data to enable these coordination algorithms to adapt to new sensed conditions. This work relies on the concurrent sensing of the environment and actuation of those sensors based on sensed data. Sensing the environment is approached as a two-layer optimization problem. Since mobile sensors in dynamic environments may move even when not actuated, sensor coordination and actuation algorithms must maintain connectivity for the sensors while ensuring those sensors are appropriately located. The algorithms and software developed consider the time scales of the sensed environment, as well as the motion capabilities of the mobile sensors. This closes the loop from sensing of the environment to actuation of the devices that perform that sensing. This work is addresses a challenging problem: the management of clean water resources. Tidally forced rivers are critical elements in the water supply for millions of Californians. By involving students from underrepresented groups, this research provides a valuable opportunity for students to develop an interest in engineering and to learn first hand about the role of science and engineering in addressing environmental issues.

本研究的目标是从静态传感到动态环境中的移动的驱动传感的转变，重点是潮汐强迫河流的传感。该方法是开发逆向建模技术来感测环境，协调算法来空间分布传感器，以及使用感测到的环境数据来使这些协调算法能够适应新的感测条件的软件。这项工作依赖于环境的同时感测和基于感测数据的这些传感器的致动。传感器的环境接近作为一个两层优化问题。由于动态环境中的移动的传感器即使在未被致动时也可能移动，因此传感器协调和致动算法必须保持传感器的连接性，同时确保这些传感器被适当地定位。开发的算法和软件考虑了感测环境的时间尺度，以及移动的传感器的运动能力。这闭合了从环境的感测到执行该感测的设备的致动的回路。这项工作是解决一个具有挑战性的问题：清洁水资源的管理。潮汐迫使河流是数百万加利福尼亚人供水的关键因素。通过让来自代表性不足的群体的学生参与进来，这项研究为学生提供了一个宝贵的机会，让他们培养对工程的兴趣，并直接了解科学和工程在解决环境问题中的作用。