Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter
Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter
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DOI：
10.1016/j.comcom.2010.10.003
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
Guiyi Wei;Y. Ling;Binfeng Guo;Bin Xiao;A. Vasilakos
中科院分区：
文献类型：
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作者：
Guiyi Wei;Y. Ling;Binfeng Guo;Bin Xiao;A. Vasilakos
在许多环境监测应用中，由于无线传感器网络周期性感知的数据通常具有较高的时间冗余性，基于预测的数据聚合是减少冗余数据通信和节省传感器节点能量的重要途径。本文提出了一种新的基于预测的数据收集协议，该协议通过设计双队列机制来同步传感器节点和汇聚节点的预测数据序列，从而减少了连续预测的累积误差。基于该协议，提出了三种基于预测的数据聚合方法：基于灰色模型的数据聚合（GMDA）、基于卡尔曼滤波的数据聚合（KFDA）和灰色模型与卡尔曼滤波相结合的数据聚合（CoGKDA）。CoGKDA结合了灰色模型快速建模的优点和卡尔曼滤波处理数据序列噪声的优点，具有预测精度高、通信开销小、计算复杂度低等优点。基于一个粮仓温湿度监测应用的真实的数据集进行了实验。实验结果表明，所提方法显著降低了通信冗余，提高了无线传感器网络的生存时间。
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.