In this paper, we design and evaluate an energy efficient data retrieval architecture for continuous aggregate queries in wireless sensor networks. We show how the modification of precision in one sensor affects the sample-reporting frequency of other sensors, and how the precisions of a group of sensors may be collectively modified to achieve the target Quality of Information (QoI) with higher energy-efficiency. The proposed Collective Adaptive Precision Setting (CAPS) architecture is then extended to exploit the observed temporal correlation among successive sensor samples for even greater energy efficiency. Detailed simulations with synthetic and real data traces demonstrate how the combination of weak consistency semantics and temporal correlation can dramatically lower the energy consumption in practical sensor environments.