Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter

Abstract

In many environmental monitoring applications, since the data periodically sensed by wireless sensor networks usually are of high temporal redundancy, prediction-based data aggregation is an important approach for reducing redundant data communications and saving sensor nodes’ energy. In this paper, a novel prediction-based data collection protocol is proposed, in which a double-queue mechanism is designed to synchronize the prediction data series of the sensor node and the sink node, and therefore, the cumulative error of continuous predictions is reduced. Based on this protocol, three prediction-based data aggregation approaches are proposed: Grey-Model-based Data Aggregation (GMDA), Kalman-Filter-based Data Aggregation (KFDA) and Combined Grey model and Kalman Filter Data Aggregation (CoGKDA). By integrating the merit of grey model in quick modeling with the advantage of Kalman Filter in processing data series noise, CoGKDA presents high prediction accuracy, low communication overhead, and relative low computational complexity. Experiments are carried out based on a real data set of a temperature and humidity monitoring application in a granary. The results show that the proposed approaches significantly reduce communication redundancy and evidently improve the lifetime of wireless sensor networks.