Dynamic scheduling management in wireless sensor networks is one of the most challenging problems in long lifetime monitoring applications. In this paper, we propose and evaluate a novel data correlation-based stochastic scheduling algorithm, called Cscan. Our system architecture integrates an empirical data prediction model with a stochastic scheduler to adjust a sensor node's operational mode. We demonstrate that substantial energy savings can be achieved while assuring that the data quality meets specified system requirements. We have evaluated our model using a light intensity measurement experiment on a Micaz testbed, which indicates that our approach works well in an actual wireless sensor network environment. We have also investigated the system performance using Wisconsin-Minnesota historical soil temperature data. The simulation results demonstrate that the system error meets specified error tolerance limits and up to a 70 percent savings in energy can be achieved in comparison to fixed probability sensing schemes.