The promise of smart environments and the Internet of Things (IoT) relies on robust sensing of diverse environmental facets. Traditional approaches rely on direct and distributed sensing, most often by measuring one particular aspect of an environment with a special purpose sensor. This approach can be costly to deploy, hard to maintain, and aesthetically and socially obtrusive. In this work, we explore the notion of general purpose sensing, wherein a single enhanced sensor can indirectly monitor a large context, without direct instrumentation of objects. Further, through what we call Synthetic Sensors, we can virtualize raw sensor data into actionable feeds, whilst simultaneously mitigating immediate privacy issues. A series of structured, formative studies informed the development of our new sensor hardware and accompanying information architecture. We deployed our system across many months and environments, the results of which show the versatility, accuracy and potential utility of our approach.

Research Team: Gierad Laput, Yang Zhang, Chris Harrison