Multidimensional Similarity In-network Query for Large-Scale Sensor Networks

The multidimensional similarity query, an essential query for information processing in sensor networks, has not received sufficient attention in the research community of sensor networks. In this paper, we study the multidimensional similarity query for large-scale sensor networks and propose a new algorithm called DIC (dimension reduction by Chebyshev polynomials). In DIC algorithm, the normalized Chebyshv coefficients are adopted as indexing and theoretic storage location of multidimensional data, and the multidimensional data are stored in the sensor nodes close to the theoretic location. A query bounding is estimated by using DIC algorithm, and query is executed inside a small zone. Inside the small zone, a new method of the itinerary-based query propagation and data aggregation is presented. The DIC algorithm does not require to preserve any index structure in sensor nodes, and also do not reply on any infrastructure structures distributed among the sensor nodes. We provide extensive experiments to evaluate the performance of the algorithm. The experimental results demonstrate that DIC can indeed enable efficient similarity queries.