MDS: Efficient Multi-dimensional Query Processing in Data-Centric WSNs

Geographical hash table (GHT) has been widely used to provide energy efficiency for data-centric storage in wireless sensor networks. Such a mechanism, however, suffers from high communication cost when we apply multi-dimensional event search in the network. In this work, we present MDS, a flexible, complete, and efficient multi-dimensional search mechanism atop traditional GHT based data-centric storage architecture. MDS utilizes bloom filters to reduce the communication cost of in-network intersection and union operations for multi-dimensional queries in wireless sensor networks. This scheme can be easily extended to support multi-dimensional range queries. Our mathematical analysis indicates the optimal settings for the bloom filters that maximize the traffic savings according to the information popularities. We conduct comprehensive simulations to evaluate our design. Results show that MDS achieves significant performance improvement in terms of energy consumptions and thus improves the applicability of the multi-dimensional search over the GHT based data-centric storage in sensor networks.