Efficient Iceberg Query Processing in Sensor Networks

Abstract

The iceberg query finds data whose aggregate values exceed a pre-specified threshold. To process an iceberg query in sensor networks, all sensor data have to be aggregated and then sensor data whose aggregate values are smaller than the threshold are eliminated. Whether a certain sensor datum is in the query result depends on the other sensor data values. Since sensor nodes are distributed, communications between sensor nodes are required to know the sensor data from the other sensor nodes. However, sensor nodes have limited energy resources and communication is a primary source of the energy consumption. Thus, reducing the communication overhead is the most critical issue in sensor networks. In this paper, we propose an energy-efficient iceberg query processing technique in sensor networks. To compactly represent the data transmitted, a lossless sensor data compression method based on the Fundamental Theorem of Arithmetic is devised. To reduce the energy consumption caused by the number of data transmitted, a filtering-based query processing method is devised. Using the temporal correlation of sensor data and the semantics of an iceberg query, a prediction model is proposed. Based on the predicted future query result, sensor nodes effectively filter out unnecessary transmissions. The experimental results confirm the effectiveness of our approach.