Robust approximate aggregation in sensor data management systems

In the emerging area of sensor-based systems, a significant challenge is to develop scalable, fault-tolerant methods to extract useful information from the data the sensors collect. An approach to this data management problem is the use of sensor database systems, which allow users to perform aggregation queries such as MIN, COUNT, and AVG on the readings of a sensor network. In addition, more advanced queries such as frequency counting and quantile estimation can be supported. Due to energy limitations in sensor-based networks, centralized data collection is generally impractical, so most systems use in-network aggregation to reduce network traffic. However, even these aggregation strategies remain bandwidth-intensive when combined with the fault-tolerant, multipath routing methods often used in these environments. To avoid this expense, we investigate the use of approximate in-network aggregation using small sketches. We present duplicate-insensitive sketching techniques that can be implemented efficiently on small sensor devices with limited hardware support and we analyze both their performance and accuracy. Finally, we present an experimental evaluation that validates the effectiveness of our methods.