Synthetic Sensors: Towards General-Purpose Sensing

The promise of smart environments and the Internet of Things (IoT) relies on robust sensing of diverse environmental facets. Traditional approaches rely on direct or distributed sensing, most often by measuring one particular aspect of an environment with special-purpose sensors. In this work, we explore the notion of general-purpose sensing, wherein a single, highly capable 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. We used a series of structured, formative studies to inform the development of 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 of this approach.

Additional media can be found on Gierad Laput's site.

This research was generously supported by Google as part of the GIoTTO Expedition Project, as well as with funding from the The David and Lucile Packard Foundation.



Laput, G., Zhang, Y. and Harrison, C. 2017. Synthetic Sensors: Towards General-Purpose Sensing. In Proceedings of the 35th Annual SIGCHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA, May 6 - 11, 2017). CHI '17. ACM, New York, NY. 3986-3999.

© Chris Harrison