Discovering routine events in sensor streams for macroscopic sensing composition

This poster abstract introduces the problem of macroscopic sensing composition, where a sensor capable to detect complex events is synthesized dynamically by a collection of simpler sensors using a data-driven approach. Our solution is geared towards discovering the structure of human activities by considering the triggering of simple sensors over a diverse set of spatial and temporal scales. The goal is to identify routines from their components by leveraging the fact that the components have the same temporal persistence as the routines themselves. To this end we have devised an algorithm for determining if an event occurs consistently within a time interval where the interval is periodic but the event is not. The goal of the algorithm is to identify events with this property and also determine the minimum interval in which they occur. Our first results using testbed data and simulations indicate that this approach can uncover components of routines by identifying which events are parts of the same routine through their temporal properties.