CAJ | 학술논문

提出了一种无线传感器网络中基于压缩感知的数据采集方法。通过分析信号压缩观测过程，提出了适合在硬件资源有限的传感器节点中实现的循环稀疏伯努利观测矩阵CSBM(Cyclic-Sparse-Bernoulli Measurement)，该矩阵使用循环稀疏矩阵与伪随机伯努利序列，采用结构化的方法构造，具有非零元素少、良好的伪随机性、硬件易于实现等优点。仿真实验表明，与其他类型的观测矩阵相比，CSBM矩阵在一定信号重构精度前提下具有更低的压缩采样比CSR( Compress Sampling Rate)。在无线传感器网络数据采集应用中，感知节点可以通过压缩观测得到更少的观测数据，能够大大减少网络通信数据量。
제출료일충무선전감기망락중기우압축감지적수거채집방법。통과분석신호압축관측과정，제출료괄합재경건자원유한적전감기절점중실현적순배희소백노리관측구진CSBM(Cyclic-Sparse-Bernoulli Measurement)，해구진사용순배희소구진여위수궤백노리서렬，채용결구화적방법구조，구유비령원소소、량호적위수궤성、경건역우실현등우점。방진실험표명，여기타류형적관측구진상비，CSBM구진재일정신호중구정도전제하구유경저적압축채양비CSR( Compress Sampling Rate)。재무선전감기망락수거채집응용중，감지절점가이통과압축관측득도경소적관측수거，능구대대감소망락통신수거량。
An efficient data collection method in wireless sensor network( WSN) was proposed based on Compressed Sensing( CS) . Firstly,the Cyclic-Sparse-Bernoulli Measurement( CSBM) matrix was presented which is suitable for application in resource-constrained sensor node. The CSBM matrix is constructed using the structured approach with cyclic-sparse matrix and Bernoulli pseudo-randomness sequence,which have a series advantages,such as less non-zero elements,good property of pseudo-randomness and easy implementation in hardware. The Simulation and exper-iment show that considering the precision of signal reconstruction, the CSBM matrix can reach the less compress sampling rate( CSR) compared to other types of measurement matrix. In the application of data collection in WSN, the sensor node can acquire less data through CS measurement,which reduce data traffic in WSN.