Cscan: A correlation-based scheduling algorithm for wireless sensor networks

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.