Efficient Iceberg Join Processing in Wireless Sensor Networks

A new class of monitoring applications is emerging, in which multiple embedded devices are deployed to sense the physical world and a large amount of data is injected into the network. Yet, existing monitoring algorithms usually output a result set that is trivial for users and too expensive for the resource-constraint network. In this paper, we study the problem of iceberg join processing in wireless sensor networks. The iceberg join query only includes a small fraction of data in its result set, yet, still contains the most 'interesting' and useful data relationships and linkages of the sensing data. The proposed algorithm SRJA is output sensitive and adopts a progressive refinement strategy for the query processing. Our algorithm first constructs flexible synopses according to the characteristics of the joining data, and then progressively refines these synopses to identify tuples that can match and meet the iceberg threshold in the joining regions. It fully utilises the iceberg threshold to filter out tuples that do not contribute to the final result set at early stages, saving lots of transmissions. Extensive experiments indicate that our algorithm gains a reduction up to 25% of message transmissions compared with other schemes.