| 摘要:考虑无线传感网中数据采集特点和能量约束性，本文将分簇路由策略融合到压缩感知采样中，提出了一种融合K均值分簇MST路由的压缩采样算法。本算法采用稀疏投影矩阵以减小投影矩阵与稀疏基之间的相关度，利用K均值分簇MST路生成数据融合树，在保证数据重构质量的基础上减少网络数据传输量。仿真结果表明，该算法可以提高网络能量效率，同时可以适应各种规模的无线传感网。 |
| 关键词: 无线传感网 压缩感知 自适应采样 最小生成树 K均值分簇 |
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| Compressive Sensing Technology Combined with K-Means Clustered MST routing for Wireless Sensor Networks |
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| Abstract:Considering the characteristics of the data collection and energy constraints in wireless sensor networks, this paper combines clustered routing strategy with compressed sensing data collection method and then proposes a compressive sensing based sampling algorithm with K-Means clustering MST routing. The proposed algorithm uses the sparse projection matrix to reduce the degree of correlation between the projection matrix and sparse matrix, using K-Means clustering MST data fusion tree, reduce the amount of data transmission in the basis to ensure the quality of the data reconstruction. The simulation results show that, this algorithm can improve the network energy efficiency, and also be suitable to all kinds of scale wireless sensor networks. |
| Key words: Wireless sensor networks Compressive sensing Adaptive sampling Minimum Spanning Tree K-Means clustering |