CAJ | 학술논문

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。
수거수집문제시무선전감망중적일개연구열점。침대현유수거수집방법적불족，제출일충기우자회귀모형적수거수집방안。수선분석감지수거희소성변화정황대우중구성능적영향，연후기우자회귀모형대압축감지중구문제진행건모，최후 sink 이용시간상관성래대중구오차진행평개，병근거중구오차요구래결정시부수요증가측량차수，종이실현대감지수거적자괄응중구。방진실험결과표명，해방법시유효적，재수거중구정도이급망락생명주기등방면요우우전통적방법。
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.