When there are multiple queries posed to the resourceconstrained wireless sensor network, it is critical to process them efficiently. In this paper, we propose a Two-Tier Multiple Query Optimization (TTMQO) scheme. The first tier, called base station optimization, adopts a cost-based approach to rewrite a set of queries into an optimized set that shares the commonality and eliminates the redundancy among the queries in the original set. The optimized queries are then injected into the wireless sensor network. In the second tier, called in-network optimization, our scheme efficiently delivers query results by taking advantage of the broadcast nature of the radio channel and sharing the sensor readings among similar queries over time and space at a finer granularity. Our experimental results indicate that our proposed TTMQO scheme offers significant improvements over the traditional single query optimization technique.
In this paper, we study the problem of processing multiple queries in a wireless sensor network. We focus on multiquery optimization at the base station level to minimize the number of radio messages in the sensor network. We adopt a cost-based approach, and develop a cost model to study the benefit of exploiting common subexpressions in queries. We also propose several optimization algorithms for both data acquisition queries and aggregation queries that intelligently rewrite multiple sensor data queries (at the base station) into "synthetic" queries to eliminate redundancy among them before they are injected into the wireless sensor network. The set of running synthetic queries is dynamically updated by the arrival of new queries as well as the termination of existing queries. We validate the effectiveness of our cost model and our experimental results indicate that our multi-query optimization strategy can provide significant performance improvements.
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