CPS: TTP Option: Synergy: Collaborative Research: Calibration of Personal Air Quality Sensors in the Field - Coping with Noise and Extending Capabilities

CPS：TTP 选项：协同：协作研究：现场校准个人空气质量传感器 - 应对噪音和扩展功能

• 批准号: 1446912
• 负责人: William Griswold
• 金额: $ 111万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446912&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Synergy; Collaborative

All cyber-physical systems (CPS) depend on properly calibrated sensors to sense the surrounding environment. Unfortunately, the current state of the art is that calibration is often a manual and expensive operation; moreover, many types of sensors, especially economical ones, must be recalibrated often. This is typically costly, performed in a lab environment, requiring that sensors be removed from service. MetaSense will reduce the cost and management burden of calibrating sensors. The basic idea is that if two sensors are co-located, then they should report similar values; if they do not, the least-recently-calibrated sensor is suspect. Building on this idea, this project will provide an autonomous system and a set of algorithms that will automate the detection of calibration issues and preform recalibration of sensors in the field, removing the need to take sensors offline and send them to a laboratory for calibration. The outcome of this project will transform the way sensors are engineered and deployed, increasing the scale of sensor network deployment. This in turn will increase the availability of environmental data for research, medical, personal, and business use. MetaSense researchers will leverage this new data to provide early warning for factors that could negatively affect health. In addition, graduate student engagement in the research will help to maintain the STEM pipeline.This project will leverage large networks of mobile sensors connected to the cloud. The cloud will enable using large data repositories and computational power to cross-reference data from different sensors and detect loss of calibration. The theory of calibration will go beyond classical models for computation and physics of CPS. The project will combine big data, machine learning, and analysis of the physics of sensors to calculate two factors that will be used in the calibration. First, MetaSense researchers will identify measurement transformations that, applied in software after the data collection, will generate calibrated results. Second, the researchers will compute the input for an on-board signal-conditioning circuit that will enable improving the sensitivity of the physical measurement. The project will contribute research results in multiple disciplines. In the field of software engineering, the project will contribute a new theory of service reconfiguration that will support new architecture and workflow languages. New technologies are needed because the recalibration will happen when the machine learning algorithms discover calibration errors, after the data has already been collected and processed. These technologies will support modifying not only the raw data in the database by applying new calibration corrections, but also the results of calculations that used the data. In the field of machine learning, the project will provide new algorithms for dealing with spatiotemporal maps of noisy sensor readings. In particular, the algorithms will work with Gaussian processes and the results of the research will provide more meaningful confidence intervals for these processes, substantially increasing the effectiveness of MetaSense models compared to the current state of the art. In the field of pervasive computing, the project will build on the existing techniques for context-aware sensing to increase the amount of information available to the machine learning algorithms for inferring calibration parameters. Adding information about the sensing context is paramount to achieve correct calibration results. For example, a sensor that measures air pollution inside a car on a highway will get very different readings if the car window is open or closed. Finally, the project will contribute innovations in sensor calibration hardware. Here, the project will contribute innovative signal-conditioning circuits that will interact with the cloud system and receive remote calibration parameters identified by the machine learning algorithms. This will be a substantial advance over current circuits based on simple feedback loops because it will have to account for the cloud and machine learning algorithms in the loop and will have to perform this more complex calibration with power and bandwidth constraints. Inclusion of graduate students in the research helps to maintain the STEM pipeline.

所有的信息物理系统（CPS）都依赖于正确校准的传感器来感知周围的环境。不幸的是，目前的技术水平是，校准通常是手动和昂贵的操作;此外，许多类型的传感器，特别是经济型传感器，必须经常重新校准。这通常成本高昂，需要在实验室环境中执行，需要将传感器从服务中移除。 MetaSense将降低校准传感器的成本和管理负担。基本思想是，如果两个传感器位于同一位置，那么它们应该报告相似的值;如果它们不这样做，则最近校准的传感器是可疑的。 基于这一想法，该项目将提供一个自主系统和一套算法，可以自动检测校准问题并在现场对传感器进行重新校准，从而无需将传感器离线并将其发送到实验室进行校准。该项目的成果将改变传感器的设计和部署方式，增加传感器网络部署的规模。这反过来将增加环境数据的可用性，用于研究，医疗，个人和商业用途。MetaSense研究人员将利用这些新数据为可能对健康产生负面影响的因素提供早期预警。 此外，研究生参与研究将有助于维护STEM管道。该项目将利用连接到云的大型移动的传感器网络。云计算将允许使用大型数据存储库和计算能力来交叉引用来自不同传感器的数据并检测校准丢失。校准理论将超越CPS的计算和物理的经典模型。该项目将结合联合收割机、大数据、机器学习和传感器物理分析，计算校准中使用的两个因素。首先，MetaSense研究人员将确定测量转换，在数据收集后应用于软件中，将生成校准结果。其次，研究人员将计算板载信号调理电路的输入，这将有助于提高物理测量的灵敏度。该项目将贡献多个学科的研究成果。在软件工程领域，该项目将贡献一种新的服务重构理论，支持新的体系结构和工作流语言。 需要新技术，因为在数据已经收集和处理之后，当机器学习算法发现校准错误时，将发生重新校准。这些技术将不仅支持通过应用新的校准校正来修改数据库中的原始数据，还支持使用这些数据的计算结果。在机器学习领域，该项目将提供新的算法来处理嘈杂传感器读数的时空图。特别是，这些算法将与高斯过程一起工作，研究结果将为这些过程提供更有意义的置信区间，与当前最先进的技术相比，大大提高了MetaSense模型的有效性。该项目将建立在现有技术的背景下，感知感测以增加机器学习算法可用于推断校准参数的信息量。添加关于感测环境的信息对于实现正确的校准结果至关重要。例如，一个测量高速公路上汽车内空气污染的传感器，如果车窗打开或关闭，会得到非常不同的读数。最后，该项目将有助于传感器校准硬件的创新。在这里，该项目将贡献创新的信号调理电路，该电路将与云系统交互，并接收由机器学习算法识别的远程校准参数。这将是基于简单反馈回路的电流电路的实质性进步，因为它必须考虑回路中的云和机器学习算法，并且必须在功率和带宽限制的情况下执行更复杂的校准。 将研究生纳入研究有助于维持STEM管道。