  Photos taken by smartphone users can accidentally contain content that is
timely and valuable to others, often in real-time. We report the system design
and evaluation of a distributed search system, Theia, for crowd-sourced
real-time content search of smartphone photos. Because smartphones are
resource-constrained, Theia incorporates two key innovations to control search
cost and improve search efficiency. Incremental Search expands search scope
incrementally and exploits user feedback. Partitioned Search leverages the
cloud to reduce the energy consumption of search in smartphones. Through user
studies, measurement studies, and field studies, we show that Theia reduces the
cost per relevant photo by an average of 59%. It reduces the energy consumption
of search by up to 55% and 81% compared to alternative strategies of executing
entirely locally or entirely in the cloud. Search results from smartphones are
obtained in seconds. Our experiments also suggest approaches to further improve
these results.
