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Sensor networks open up new opportunities to observe and interact with the physical world around us. Although there have been many advances in sensor network applications and technology, sensor networks still suffer from the major problems of limited energy due to the difficulties of replacing batteries when they ran out. We aim to improve the efficient processing of continuous queries, by which query results have to be generated according to the sampling rate specified by the user for an extended period of time. Two types of continuous queries, namely queries requiring data from all sensor nodes and selected nodes, are considered in this work. We adopt Kalman filter to correct and predict sensing data, which offers opportunities to dynamically adjust the sampling frequency of each sensor. The proposed resampling approach can effectively reduce the energy consumption without sacrificing users' desired accuracy, as evaluated using empirical data collected from a real sensor network.