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This paper studies the issues of energy-efficient query optimization for wireless sensor networks. Different from existing query optimization techniques that consider only query plans for extracting data from sensors at individual nodes, our approach takes into account both of the sensing and communication cost in query plans. Central to our study is a cost-based analysis, based on which the energy cost of candidate plans for a given query are estimated to determine a query plan that is likely to consume the least energy for execution. Simulation results show that the query plan chosen in our approach consumes significantly less energy than an approach that optimizes on sensing cost only.