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

建筑物是复杂的系统，对人类健康、生产力、舒适度和能源消耗有着深远的影响。智能建筑技术通过将传感器数据应用于更明智和精确的建筑运营，有望改善建筑运营的许多方面。 智能建筑是实现可持续智慧城市的一个重要方面。 智能建筑的挑战之一是在建筑物中选择、放置和安装多个传感器。 这可能是既昂贵又耗时的过程。 传感器的不良放置可能对获得能量节省的能力具有显著的不利影响。 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，该技术可根据其他建筑物中收集的传感器数据估计一座建筑物的传感器数据。该技术利用了传感器数据中的模式，这些模式来自建筑物设计和建造中的常见模式。 如果成功，这项技术将在智能建筑应用的可扩展性方面产生根本性的转变，这样它们就可以应用于新的建筑物，而无需安装新的仪器。此外，基础数学技术将推广到建筑环境的其他方面，其中设计，施工或使用模式在传感器数据中创建模式。智能建筑技术有望通过收集和分析传感器数据来改善建筑运营的许多方面，以支持明智和精确的建筑运营。然而，智能建筑应用的采用受到以下事实的抑制：必须在每个建筑物中安装新的传感器，并且传感器放置的优化可能是困难的并且需要大量的实验和努力。 该研究项目开发了一种基于协作传感概念的创新方法。在该方法中，基于在其他建筑物中收集的传感器数据来估计一个建筑物的传感器数据。基本前提是建筑物的常见设计和施工模式在其传感器数据中创建重复结构。因此，稀疏感测基础可用于表示来自广泛范围的建筑物的传感器数据。建筑物的模型可以从这个传感基础上构建，只使用少量的数据，如公用事业仪表读数，气候区和平方英尺。然后，可以使用该低维模型基于在其他建筑物中收集的高保真数据来重建建筑物的传感器数据。这种方法旨在创造一种新的模式，在这种模式中，智能建筑功能可以应用于新的建筑物，而无需安装专门的仪器。使用来自100多座建筑物的公开分计量数据进行的初步测试表明，这种方法不仅更具可扩展性，而且有时比最先进的替代方案更准确。 如果成功，这项研究将为智能建筑应用的可扩展性带来根本性的转变。基础数学技术将推广到构建环境的其他方面，其中设计，构造或使用模式在传感器数据中创建模式。这些技术将被封装在一个Web服务中，允许世界上任何地方的人们将所提出的技术应用到他们自己的建筑中。该项目将有助于国家科学基金会的研究和教育的双重使命，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.