基于压缩感知的无线传感器网络动态采样方法

基于固定采样率的无线传感网（WSN）压缩感知（CS）在收集随时间变化的数据时难以获得满意的数据恢复精度。针对该问题，提出了一种基于数据预测和采样率反馈控制的动态采样方法。首先，汇聚节点通过分析当前采样时段与上一采样时段获取数据的线性度量指标，预测数据的变化趋势；然后，根据预测结果计算感知节点未来的采样率，并通过反馈控制机制对感知节点的采样过程进行动态调节。实验结果表明，相比基于目前广泛采用的基于固定采样率的无线传感网压缩感知数据收集方法，该方法能够有效提高压缩数据的恢复精度。

Dynamic sampling method for wireless sensor network based on compressive sensing

It is hard to obtain a satisfactory reconstructive quality while compressing time-varying signals monitored by Wireless Sensor Network (WSN) using Compressive Sensing (CS), therefore a novel dynamic sampling method based on data prediction and sampling rate feedback control was proposed. Firstly, the sink node acquired the changing trend by analyzing the liner degree differences between current reconstructed data and last reconstructed data. Then the sink node calculated the suitable sampling rate according to the changing trend and fed back the result to sensors to dynamically adjust their sampling process. The experimental results show that the proposed dynamic sampling method can acquire higher reconstructed data accuracy than the CS data gathering method based on static sampling rate for WSN.