CPS: TTP Option: Breakthrough: Collaborative Sensing: An Approach for Immediately Scalable Sensing in Buildings

建筑是一个复杂的系统，对人类的健康、生产力、舒适度和能源消耗都有深远的影响。智能建筑技术承诺通过将传感器数据应用于更知情和更精确的建筑操作，来改善建筑操作的许多方面。智能建筑是实现可持续智能城市的一个重要方面。智能建筑的挑战之一是在建筑中选择、放置和安装多个传感器。这可能是一个既昂贵又耗时的过程。传感器放置不当可能会对获得节能的能力产生重大不利影响。该研究项目旨在通过开发称为协作传感的新技术来提高智能建筑应用的可扩展性，该技术基于在其他建筑中收集的传感器数据来估计一栋建筑的传感器数据。这项技术利用了传感器数据中的模式，这些模式是建筑设计和施工中常见模式的结果。如果成功，这项技术将从根本上改变智能建筑应用程序的可伸缩性，这样它们就可以应用于新建筑，而不需要安装新的仪器。此外，基本的数学技术将推广到建筑环境的其他方面，在这些方面，设计、施工或使用模式在传感器数据中创建模式。智能建筑技术承诺通过收集和分析传感器数据来改善建筑运营的许多方面，以支持知情和精确的建筑运营。然而，智能建筑应用的采用受到限制，因为必须在每栋建筑中安装新的传感器，并且传感器布置的优化可能很困难，需要大量的实验和努力。这项研究项目开发了一种基于协作感知概念的创新方法。在这种方法中，一栋建筑的传感器数据是根据在其他建筑中收集的传感器数据来估计的。基本前提是，建筑物的常见设计和建造模式在其传感器数据中创建了一个重复结构。因此，稀疏传感基础可用于表示来自大范围建筑物的传感器数据。只需使用少量数据，如公用事业仪表读数、气候区域和面积，就可以在此基础上构建建筑模型。然后，该低维模型可用于根据在其他建筑物中收集的高保真数据来重建该建筑物的传感器数据。这种方法的目的是转变为一种新的模式，在这种模式下，智能建筑功能可以应用于新建筑，而不需要安装专门的仪器。使用可公开获得的100‘S建筑的分表数据进行的初步测试表明，该方法不仅具有更高的可扩展性，有时还比最先进的替代方法更准确。如果成功，这项研究将在智能建筑应用的可扩展性方面产生根本性转变。基本的数学技术将推广到建筑环境的其他方面，在这些方面，设计、施工或使用中的模式将在传感器数据中创建模式。这些技术将被封装在Web服务中，允许世界上任何地方的人们将建议的技术应用到他们自己的建筑中。该项目将为国家科学基金会的研究和教育双重使命做出贡献，研究生和本科生研究人员将参与这项研究的所有阶段。

Buildings are complex systems with profound impact on human health, productivity, comfort, and energy consumption. Smart building technology promises to improve many aspects of building operation by applying sensor data toward more informed and precise building operation. Smart buildings are one important dimension of enabling sustainable Smart Cities. One of the challenges in smart buildings is the selection, placement, and installation of multiple sensors in the building. This can be both an expensive and time consuming process. Poor placement of sensors can have a significant adverse impact on the ability to obtain energy savings. This research project aims to improve the scalability of smart building applications by developing new techniques called collaborative sensing that estimate the sensor data of one building based on sensor data collected in other buildings. The technique exploits patterns in sensor data that result from common patterns in the design and construction of buildings. If successful, this technique will create a fundamental shift in the scalability of smart building applications, such that they can be applied to a new building without the need to install new instrumentation. Additionally, the underlying mathematical techniques will generalize to other aspects of the built environment where patterns in design, construction, or usage create patterns in sensor data.Smart building technology promises to improve many aspects of building operation by collecting and analyzing sensor data to support informed and precise building operation. However, adoption of smart building applications is inhibited by the fact that new sensors must be installed in every building, and that optimization of sensor placement may be difficult and require significant experimentation and effort. This research project develops an innovative approach based on the notion of collaborative sensing. In this approach the sensor data of one building is estimated based on sensor data collected in other buildings. The basic premise is that common design and construction patterns for buildings create a repeating structure in their sensor data. Thus, a sparse sensing basis can be used to represent sensor data from a broad range of buildings. A model of a building can be constructed from this sensing basis using only a small amount of data, such as utility meter readings, climate zone, and square footage. This low-dimensionality model can then be used to reconstruct sensor data for the building based on high-fidelity data collected in other buildings. This approach aims to create a shift to a new paradigm in which smart building functionality can be applied to new buildings without the need to install specialized instrumentation. Preliminary testing using publicly available sub-metering data from 100's of buildings indicate that this approach is not only more scalable but also sometimes more accurate than state-of-the-art alternatives. If successful, this research will create a fundamental shift in the scalability of smart building applications. The underlying mathematical techniques will generalize to other aspects of the built environment where patterns in design, construction, or usage create patterns in sensor data. These techniques will be encapsulated in a Web service that allows people anywhere in the world to apply the proposed techniques to their own building. The project will contribute to the National Science Foundation's dual missions of research and education, and both graduate and undergraduate researchers will be involved in all phases of this research.