Approximate Aggregation Techniques for Sensor Databases

• Authors: Jeffrey Considine;Feifei Li;George Kollios;John Byers
• Venue: ICDE '04 Proceedings of the 20th International Conference on Data Engineering
• Year: 2004

In the emerging area of sensor-based systems, a significantchallenge is to develop scalable, fault-tolerantmethods to extract useful information from the data thesensors collect.An approach to this data managementproblem is the use of sensor database systems, exemplifiedby TinyDB and Cougar, which allow users to performaggregation queries such as MIN, COUNT andAVG on a sensor network.Due to power and range constraints,centralized approaches are generally impractical,so most systems use in-network aggregation to reducenetwork traffic.However, these aggregation strategiesbecome bandwidth-intensive when combined with thefault-tolerant, multi-path routing methods often used inthese environments.For example, duplicate-sensitive aggregatessuch as SUM cannot be computed exactly usingsubstantially less bandwidth than explicit enumeration.To avoid this expense, we investigate the use of approximatein-network aggregation using small sketches.Our contributions are as follows: 1) we generalize wellknown duplicate-insensitive sketches for approximatingCOUNT to handle SUM, 2) we present and analyze methodsfor using sketches to produce accurate results withlow communication and computation overhead, and 3)we present an extensive experimental validation of ourmethods.