摘要
针对无线传感网络监测信息具有很高时间相关性,给出一种基于动态灰色预测模型的数据融合算法。对节点采集的历史数据,应用动态灰色模型预测其未来数据值,若预测误差大于设定阈值,传输本轮数据,若预测误差小于设定阈值,不传输本轮数据,以此减少网络数据传输量。仿真实验表明,与未采用数据融合的低功耗自适应集簇分层协议算法相比,所给算法的数据传输量可减少34%,网络寿命可延长39%。
As the monitoring informations of wireless sensor networks is of high temporal correlation,a data fusion algorithm based on dynamic grey prediction model（DG-DFA）is proposed.The dynamic grey model is used to deal with the historical data,and predict the future values of all nodes.If the prediction error is greater than the threshold,the current round data is transmitted,otherwise,the transmission is given up,thus,the amount of network data transmission can be reduced.Simulation experiments show that,compared with the low energy adaptive clustering hierarchy（LEACH）algorithm not using data fusion,the data transfer quantity of the given algorithm DG-DFA can be reduced by 34%,and the network lifetime can be extended by 39%.
出处
《西安邮电大学学报》
2016年第6期103-107,共5页
Journal of Xi’an University of Posts and Telecommunications
基金
国家自然科学基金资助项目(61202490)
陕西省自然科学基础研究计划资助项目(2014JM2-6117)
陕西省教育厅科学研究计划资助项目(15JK1654)
