Approximate Aggregation Techniques for Sensor Databases

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, exemplified by TinyDB and Cougar, which allow users to perform aggregation queries such as MIN, COUNT and AVG on a sensor network. Due to power and range constraints, centralized approaches are generally impractical, so most systems use in-network aggregation to reduce network traffic. However, these aggregation strategies become bandwidth-intensive when combined with the fault-tolerant, multi-path routing methods often used in these environments. For example, duplicate-sensitive aggregates such as SUM cannot be computed exactly using substantially less bandwidth than explicit enumeration. To avoid this expense, we investigate the use of approximate in-network aggregation using small sketches. Our contributions are as follow...

• Type: Conference
• Year: 2004
• Where: ICDE
• Authors: Jeffrey Considine, Feifei Li, George Kollios, John W. Byers
• Keywords: Aggregation Queries, Approximate In-network Aggregation, Database, ICDE 2004, Multi-path Routing Methods