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: 1446899
• 负责人: Michael Hannigan
• 金额: $ 29万
• 依托单位: University of Colorado at Boulder
• 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=1446899&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.

所有网络物理系统（CPS）都取决于正确校准的传感器来感知周围环境。不幸的是，目前的现状是校准通常是手动且昂贵的操作。此外，必须经常重新校准许多类型的传感器，尤其是经济的传感器。在实验室环境中执行，这通常是昂贵的，要求将传感器从服务中删除。 元索将减少校准传感器的成本和管理负担。基本思想是，如果两个传感器是共同置于的，则应报告相似的值。如果没有，则怀疑最不校准的传感器。 在这个想法的基础上，该项目将提供一个自主系统和一组算法，该算法将自动化校准问题的检测并预先对传感器进行预启动，从而消除将传感器离线使传感器的需求，并将其发送到实验室进行校准。该项目的结果将改变传感器设计和部署的方式，从而增加传感器网络部署的规模。反过来，这将增加用于研究，医疗，个人和业务使用的环境数据的可用性。跨索斯研究人员将利用这些新数据来提供可能对健康产生负面影响的因素的预警。 此外，研究生参与研究将有助于维持STEM管道。该项目将利用与云连接的大型移动传感器网络。云将使用大型数据存储库和计算能力启用，以从不同传感器进行交叉引用数据并检测校准丢失。校准理论将超越用于CP的计算和物理的经典模型。该项目将结合大数据，机器学习和对传感器物理学的分析，以计算将在校准中使用的两个因素。首先，跨索斯研究人员将确定在数据收集后在软件中应用的测量转换，将产生校准的结果。其次，研究人员将计算出机上信号调节电路的输入，以提高物理测量的灵敏度。该项目将为多个学科提供研究结果。在软件工程领域，该项目将贡献一种新的服务重新配置理论，该理论将支持新的体系结构和工作流语言。 需要新技术，因为重新校准将在机器学习算法发现校准误差（在收集和处理数据后）时发生。这些技术将不仅通过应用新的校准校正来修改数据库中的原始数据，还可以支持使用数据的计算结果。在机器学习领域，该项目将为处理嘈杂传感器读数的时空图提供新的算法。尤其是，算法将与高斯流程一起使用，研究结果将为这些过程提供更有意义的置信区间，与当前的技术状态相比，大大提高了跨索斯模型的有效性。在普遍计算的领域中，该项目将基于现有技术，用于上下文感知感知，以增加机器学习算法可用的信息量以推断校准参数。添加有关感应上下文的信息对于获得正确的校准结果至关重要。例如，如果汽车窗口打开或关闭，则测量高速公路上汽车内空气污染的传感器将获得非常不同的读数。最后，该项目将在传感器校准硬件中贡献创新。在这里，该项目将贡献创新的信号调节电路，这些电路将与云系统相互作用并接收机器学习算法确定的远程校准参数。基于简单的反馈循环，这将是比当前电路的重大进步，因为它必须考虑循环中的云和机器学习算法，并且必须通过功率和带宽约束执行更复杂的校准。 将研究生纳入研究有助于维持STEM管道。