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This paper introduces Tacit Consent (TaCo), a technique that exploits spatial correlation in Wireless Sensor Networks in order to reduce energy consumption while maintaining a very high accuracy on the measured data. In fact, nodes densely deployed in a field of interest are proved to sense highly correlated data, thus this intrinsic redundancy can be exploited to predict the neighbors' measurements by defining custom estimation functions, which aim at replicating the relationship between the data sensed by two nodes. To exploit spatial correlation, TaCo splits the nodes into two groups: representative and member nodes. Representative nodes directly transmit their measurements, while member nodes use overhearing to understand whether additional information is required. If the estimation function correctly predicts the measurements of the member nodes, they tacitly consent the estimation without performing any transmission. Differently from the other state-of-the-art approaches, such as the YEAST algorithm, TaCo can be used on top of existing routing and clustering protocols. Experimental results prove that TaCo is able to drastically reduce the energy consumption of the network when a high precision of the measured data is required.