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

所有网络物理系统（CPS）都依赖于适当校准的传感器来感知周围环境。不幸的是，目前的技术状态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。这通常是昂贵的，在实验室环境中执行，需要从服务中移除传感器。MetaSense将降低校准传感器的成本和管理负担。基本思想是，如果两个传感器共存，那么它们应该报告相似的值；如果他们不这样做，最近校准的传感器是可疑的。基于这个想法，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，从而消除了将传感器离线并将其发送到实验室进行校准的需要。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持STEM管道。该项目将利用连接到云的大型移动传感器网络。云将允许使用大型数据存储库和计算能力来交叉参考来自不同传感器的数据并检测校准丢失。校正理论将超越经典模型的计算和物理CPS。该项目将结合大数据、机器学习和传感器物理分析来计算将用于校准的两个因素。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，将产生校准结果。其次，研究人员将计算机载信号调理电路的输入，这将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，该项目将提供一个新的服务重构理论，该理论将支持新的体系结构和工作流语言。需要新技术，因为当机器学习算法在数据收集和处理后发现校准错误时，就会进行重新校准。这些技术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，而且还支持使用这些数据的计算结果。在机器学习领域，该项目将为处理噪声传感器读数的时空地图提供新的算法。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，与目前的技术水平相比，大大提高了MetaSense模型的有效性。在普适计算领域，该项目将建立在现有的上下文感知技术的基础上，以增加用于推断校准参数的机器学习算法的可用信息量。添加有关传感环境的信息对于获得正确的校准结果至关重要。例如，在高速公路上测量汽车内部空气污染的传感器，在车窗打开或关闭时，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，该项目将提供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。与基于简单反馈回路的现有电路相比，这将是一个实质性的进步，因为它必须考虑到回路中的云和机器学习算法，并且必须在功率和带宽限制下执行更复杂的校准。在研究中纳入研究生有助于维持STEM管道。

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.