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Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysis. Since sensors are resource constrained, often sensor data is collected into a sensor database that resides at (more powerful) servers. A natural tradeoff exists between the sensor resources (bandwidth, energy) consumed and the quality of data collected at the server. Blindly transmitting sensor updates at a fixed periodicity to the server results in a suboptimal solution due to the differences in stability of sensor values and due to the varying application needs that impose different quality requirements across sensors. We propose adaptive data collection mechanisms for sensor environments that adjusts to these variations while at the same time optimizing the energy consumption of sensors. Our experimental results show significant energy savings compared to the naive approach to data collection.