In order to reduce the contradiction between the network energy consumption and the accuracy of the data,we consider the spatial and temporal correlation of the data collected by the network and propose an method of adaptive sampling and using compressed sensing.Wireless sensor data compression based on the traditional compressed sensing only sampling data of a few nodes and the incidents detected by those not sampled nodes maybe be ignored.This paper detect all nodes'' data and compress,so it can effectively avoid failure report.According to the characteristics of time correlation,this paper adopts adaptive sampling frequency method based on variance analysis ANOVA(Analysis of Variance)and take nodes'' residual energy into consideration to reduce the smooth signal acquisition and equilibrium the energy consumption of network.On the basis of LEACH protocol,the data of cluster is compressed and transmitted to the Sink node to reduce the overall energy consumption of the network.In order to reduce the failure report caused by the adaptive sampling and traditional compressed sensing method,an improved local event monitoring algorithm sliding window local event monitoring SW-LED(Sliding Window-Local Event Detection)algorithm is proposed,which realizes real-time and accurate anomaly detection and early warning.The experimental results show that this method can effectively balance the nodes'' energy consumption to improve the network lifetime,ensuring the accuracy of the data and the recognition rate of abnormal situation is also greatly improved.%针对降低无线传感网能耗和保证数据精度之间的矛盾,提出了自适应采样数据并利用压缩感知进行压缩的方法.传统的基于压缩感知的无线传感器数据压缩,只采样部分节点的数据,对于未被采样节点感知到的突发事件很有可能发生漏检情况.本文方法检测所有节点上传的数据再进行压缩,可以有效避免漏检情况的发生.根据信号具有时间相关性的特点,本文采用基于方差分析ANOVA(Analysis of Variance)原理改进的传感器自适应采样频率方法,并考虑节点剩余能量,减少平稳信号的采集次数,均衡网络节点能耗.在LEACH协议基础上,对簇内数据进行压缩感知的方法对数据进行压缩从而减少数据的空间相关性并传输到汇聚节点,以减少网络整体的能量消耗.针对可能的漏报情况,提出一种改进的局部事件监测算法-滑动窗口局部事件监测SW-LED(Sliding Window-Local Event Detection)算法,实现了实时准确的异常检测和预警.实验结果表明本文方法既可以有效的均衡网络节点能耗以提高网络生存周期,同时保证了数据的精度,对于异常情况的识别率也有很大的提高.
展开▼
