无线传感网中一种基于自回归模型的数据收集方案

A DATA COLLECTION SCHEME IN WIRELESS SENSOR NETWORKS BASED ON AUTO REGRESSIVE MODEL

数据收集问题是无线传感网中的一个研究热点。针对现有数据收集方法的不足,提出一种基于自回归模型的数据收集方案。首先分析感知数据稀疏性变化情况对于重构性能的影响,然后基于自回归模型对压缩感知重构问题进行建模,最后sink利用时间相关性来对重构误差进行评价,并根据重构误差要求来决定是否需要增加测量次数,从而实现对感知数据的自适应重构。仿真实验结果表明,该方法是有效的,在数据重构精度以及网络生命周期等方面要优于传统的方法。

Data collection is a hot topic in wireless sensor networks currently. Aiming at the disadvantage of existing data collection methods, we propose an AR model-based data collection scheme. First, the scheme analyses the impact of sparsity variation of sensitive data on reconstruction performance, and then models the compressed sensing reconstruction based on the AR model, finally, the sink evaluates the reconstruction error using the temporal correlation, and decides whether or not to increase the times of measurements according to the requirements of reconstruction error, so as to realise the adaptive reconstruction of sensing data. Simulation experimental results show that our method is effective, and is superior to traditional methods in terms of the data reconstruction accuracy and the lifecycle of network.