一种传感器网络不确定感知数据挖掘方法研究

Research on method of uncertain perception data mining in sensor network

对无线传感器网络中的不确定感知数据的优化挖掘算法设计,提高传感器感知层对数据信息的采集和收发能力。传统方法采用子空间重构特征分解的数据挖掘方法,随着传感器网络中的不确定数据干扰的增强,对数据的采集精度有所下降。提出一种基于滑动时间窗口重排和时频特征提取的传感器网络不确定感知数据挖掘方法。构建传感器网络的数据采集模型,对采集的不确定感知数据进行信号模型构建和数据结构子空间重建,采用滑动时间窗口重排方法进行数据降噪挖掘,提取数据信号流中的时频特征,实现数据挖掘算法改进。仿真结果表明,采用该方法进行传感器网络不确定感知数据挖掘方法进行数据检测的准确概率较高,降低了误检率和虚警率,数据挖掘的抗干扰性能较好,展示了较好的应用价值。

The design of optimal mining algorithm for uncertain data in wireless sensor networks is designed to improve the ability of sensor data acquisition and sending and receiving. In traditional method, the data mining method is used to reconstruct the feature of subspace, and the precision of data acquisition is decreased with the increase of the uncertain data in the sensor network. A data mining method based on sliding time window and time frequency feature extraction for sensor networks is proposed. Construction of sensor network data acquisition model is obtained, uncertain sensing data signal model construction and data structure subspace reconstruction for acquisition by sliding time window rearrangement method for noise reduction of data mining, data signal flow in the time-frequency features are extracted, realize data mining algorithm is improved. Simulation results show that using the method of sensor networks uncertain perception data mining methods for testing data accurate higher probability and lower false detection rate and false alarm rate, data mining of anti-jamming performance better, showing the good application value.