Abstract

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. Centralized data collection and analysis adversely impact sensor node lifetime. Previous sensor network research has, therefore, focused on in network aggregation and query processing, but has done so for applications where the features of interest are known a priori. When features are not known a priori, as is the case with many scientific applications in dense sensor arrays, efficient support for multi-resolution storage and iterative, drill-down queries is essential. Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that: a) wavelet-based hierarchical summarization provides accurate responses to a broad spectrum of spatio-temporal queries with low communication overhead; and b)in a storage-constrained environment, graceful query degradation over time is achieved by networked aging of summaries, such that more useful summaries are retained longer. To our knowledge, no one has examined data aging in the context of highly distributed sensor systems.

Date

2003

Authors

Deepak Ganesan, Ben Greenstein, Denis Perelyubskiy, Deborah Estrin, John Heidemann