基于预测模型的传感器网络近似数据采集算法

An approximate data collection algorithm based on prediction model in sensor networks

基于模型的数据采集技术可以有效抑制不必要的数据传输,节省能量开销,已经在传感器网络中得到广泛应用。对传统基于模型的数据采集进行了改进,提出基于卡尔曼滤波器的近似数据采样算法ADCA。ADCA可以在一定误差范围内有效获取数据。空间相近的节点被组织成簇,簇头和成员分别建立卡尔曼滤波模型,并保存对方的镜像模型。簇头节点可以为成员节点产生近似的数据,所以用户查询可以通过簇头来回答。实验表明ADCA具有较好的性能。

The model-based data collection technology which can inhibit unnecessary data transportation and save energy effectively has been widely applied in sensor networks.We improve the traditional model-based data collection technology and put forth an approximate data collection algorithm based on the Kalman filter,called ADCA,which can collect data effectively within a given range of error.In ADCA,sensor nodes are organized as clusters.Cluster header and cluster members build the Kalman filter respectively and save their mirror models.Cluster header can produce approximate data for cluster members,so some user queries can be answered by cluster header.Experiments show that ADCA has good performance.