基于压缩感知的土壤呼吸监测传感网动态采样调度策略

Abstract

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.