Dynamic sampling scheduling policy for soil respiration monitoring sensor networks based on compressive sensing
Dynamic sampling scheduling policy for soil respiration monitoring sensor networks based on compressive sensing
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DOI：
10.1360/112013-118
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Guoying Wang;YuJia Jiang;Lufeng Mo;Yanfeng Sun;Guomo Zhou
中科院分区：
文献类型：
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作者：
Guoying Wang;YuJia Jiang;Lufeng Mo;Yanfeng Sun;Guomo Zhou
土壤呼吸监测传感器网络通常部署在电力或人工无法直接干预的地方，土壤呼吸的测量复杂且高能耗。因此，我们希望在保证重建精度的前提下，尽量减少土壤呼吸测量的次数。基于土壤呼吸物理过程的时间相关性，利用压缩感知理论可以实现采样调度。本文提出了一种基于压缩感知的分段动态采样调度策略。利用对前期测量数据分析得到的先验知识，对测量周期内的数据序列进行划分和线性化处理。然后根据数据的线性程度确定每一段的动态采样率，在此基础上构造测量矩阵进行采样，并利用土壤呼吸测量仪重构测量矩阵进行压缩传感处理。实验结果表明，在平均采样率相同的情况下，分段动态采样策略比静态采样策略具有更好的重建质量。也就是说，在给定重构误差阈值的情况下，所提出的动态采样策略需要较小的采样率。虽然动态采样率的计算可能会消耗一些功率，但是采样率的降低可以节省更多的功率。基于压缩感知的分段动态采样策略在土壤呼吸监测传感器网络中进行了实验和分析，但在采样调度和节能问题上，也可供类似应用参考和借鉴。
Sensor networks for soil respiration monitoring are usually deployed in the elds where electric or manual intervention cannot be accessed directly, and the measurement of soil respiration is complex and highly energy-consuming. Therefore, we hope to minimize the number of soil respiration measurements on the premise of reconstruction accuracy. Sampling scheduling can be realized using compressive sensing theory on the basis of temporal correlation of the physical process of soil respiration. Here we propose a segmental dynamic sampling scheduling policy based on compressive sensing. Using a prior knowledge obtained by means of analysis on the earlier measurement data, the data serial in measurement period is partitioned and linear tted. Then the dynamic sampling rate of each segment is determined according to the linear degree of data in the segment, based on which the measurement matrix is constructed for the sampling and reconstructed for compressive sensing process using the soil respiration measuring instrument. The experimental result shows that the proposed segmental dynamic sampling policy can lead to better reconstructive quality than static sampling policy of the same average sampling rate. That is to say, the proposed dynamic sampling policy needs smaller sampling rate if the reconstructive error threshold is given. The reduction of sampling rate can save more power although the calculation of dynamic sampling rate may consume some power. The proposed segmental dynamic sampling policy based on compressive sensing can also be referenced and potentially used by similar applications for the sampling scheduling and power-saving issues, although it is experimented and analyzed in the soil respiration monitoring sensor networks.