Gradient-Based Aggregation in Forest of Sensors (GrAFS)

Abstract

In several sensing applications the parameter being sensed exhibits a high spatial correlation. For example, if the temperature of a region is being monitored, there are distinct hot and cold spots. The area close to the hot spots is usually warmer than average, with a temperature gradient between the hot and cold spots. We exploit this correlation of sensor data to form a forest of logical trees, with the trees collectively spanning all the sensor nodes. The root of a tree corresponds to a sensor reporting the local peak value. The tree nodes represent the value gradient: each node's sensed value is smaller than that of its parent, and greater than that of its children. GrAFS provides a mechanism to maintain some information at the local peaks and the sink. Using this information the sink can answer several queries either directly, or by probing the region of the sensor field that holds the answer. Thus, queries can be answered in a time and/or bandwidth efficient manner. The GrAFS approach to data aggregation can easily adapt to changes in the spatial distribution of sensed values, and also cope with message losses and sensor node failures. Implementation on MICA2 motes and simulation experiments conducted using TinyOS quantify the performance of GrAFS.