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.