Energy-efficient data organization and query processing in sensor networks

Recent sensor networks research has produced a class of data storage and query processing techniques called Data-Centric Storage that leverages locality-preserving distributed indexes like DIM, DIFS, and GHT to efficiently answer multi-dimensional range and range-aggregate queries. These distributed indexes offer a rich design space of a) logical decompositions of sensor relation schema into indexes, as well as b) physical mappings of these indexes onto sensors. In this poster, we explore this space for energy-efficient data organizations (logical and physical mappings of tuples and attributes to sensor nodes) and devise purely local query optimization techniques for processing queries that span such decomposed relations. We propose four design techniques: (a) fully decomposing the base sensor relation into distinct sub-relations, (b) spatially partitioning these sub-relations across the sensornet, (c) localized query planning and optimization to find fully decentralized optimal join orders, and (d) locally caching join results. Together, these optimizations reduce the overall network energy consumption by 4 times or more when compared against the standard single multi-dimensional distributed index on a variety of synthetic query workloads simulated over both synthetic and real-world datasets. We validate the feasibility of our approach by implementing a functional prototype of our data organizer and query processor on Mica2 motes and observing comparable message cost savings.