Many environmental phenomena (e.g., changes in global levels of atmospheric carbon dioxide) can be modeled as variations of attributes over regions of space and time, called dynamic spatial fields. The goal of this project is to provide efficient ways for sensor networks to monitor such fields, and to report significant changes in them. The focus is on "qualitative" changes, such as splitting of areas or emergence of holes in a region of study. The approach is to develop qualitative and topological methods to deal with changes. Qualitative properties form a small, discrete space, whereas quantitative values form a large, continuous space, and this enables efficiencies to be gained over traditional quantitative methods. The combinatorial map model of the spatial embedding of the sensor network is rich enough so that for each sensor, its position, and the distances and bearings of neighboring sensors, are easily computed. The sensors are "responsive" to changes to the spatial field, so that sensors are activated in the vicinity of "interesting" developments in the field, while sensors are deactivated in quiescent locations. All computation and message passing is "local", with no centralized control. Optimization is addressed through use of techniques in qualitative representation and reasoning, and efficient update through a dynamic and responsive underlying spatial framework. Effective deployment of very large arrays of sensors for environmental monitoring has important scientific and societal benefits. The project is integrated with the NSF IGERT program on Sensor Science, Engineering, and Informatics at the University of Maine, which will enhance educational and outreach opportunities. The project Web site (www.spatial.maine.edu/~worboys/sensors.html) will be used for broad results dissemination.
