Adaptive nodes scheduling approach for clustered sensor networks

Energy efficiency is an important issue in wireless sensor networks. One available power saving strategy is having only a portion of nodes work, but this would always compromise data quality as a result. In this paper, we propose an adaptive nodes scheduling approach (ADNS) to conserve energy while maintaining the overall data quality. ADNS selects a subset of nodes to be active and puts the others into sleep mode to save energy. An efficient active node selection (ANS) algorithm is presented. Using the spatial correlation among sensor readings as the prediction model, the values of sleep nodes are predicted by data collected from active nodes to ensure the data quality. In order to maintain the data quality throughout network operation, the prediction errors are validated timely and prediction models are adaptively reconstructed if necessary. We evaluate ADNS on a real-world sensor network data set and validate its effectiveness.