融合K均值分簇MST路由的无线传感网压缩采样技术 |
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引用本文: | 张美燕,蔡文郁.融合K均值分簇MST路由的无线传感网压缩采样技术[J].传感技术学报,2015,28(9):1402-1407. |
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作者姓名: | 张美燕 蔡文郁 |
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作者单位: | 1. 浙江水利水电学院电气工程系,杭州,310018;2. 杭州电子科技大学电子信息学院,杭州,310018 |
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基金项目: | 浙江省自然科学基金,国家自然科学基金 |
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摘 要: | 考虑无线传感网中数据采集特点和能量约束性,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.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(Mini?mum Spanning Tree)机制构造数据融合树,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,算法可以提高网络能量使用效率,同时可以适应各种规模的无线传感网.
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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 |
Compressive Sensing Technology Combined with K-Means Clustered MST routing for Wireless Sensor Networks |
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Abstract: | Considering the special characteristics of data collection and energy constraints of wireless sensor net-works,the paper combines clustered routing strategy with compressed sensing data collection method and then pro-poses a compressed sensing based compressive sampling algorithm with K-Means clustering MST(Minimum Span-ning Tree)routing. The proposed algorithm uses the sparse projection matrix in order to reduce the correlation de-gree value between the projection matrix and sparse matrix so as to reduce the amount of data transmission in the ba-sis to ensure the quality of the data reconstruction by using K-Means clustering MST data fusion tree. The simula-tion results show that this algorithm can improve the network energy usage efficiency,and also be suitable to all kinds of scale wireless sensor networks. |
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Keywords: | wireless sensor networks compressive sensing adaptive sampling minimum spanning tree K-Means clustering |
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