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Dynamic scheduling management in wireless sensor networks is one of the most challenging problems in long lifetime monitoring applications. In this paper, the authors propose and evaluate a novel data correlation-based stochastic scheduling algorithm, called Cscan. Their system architecture integrates an empirical data prediction model with a stochastic scheduler to adjust a sensor node's operational mode. They demonstrate that substantial energy savings can be achieved while assuring that the data quality meets specified system requirements. They have evaluated their model using a light intensity measurement experiment on a Micaz testbed, which indicates that their approach works well in an actual wireless sensor network environment.
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