利用现场数据设计传感器网络

S. K. Khandani, M. Kalantari
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引用次数: 4

摘要

分布式传感和数据采集在现场应用是一个劳动密集型和昂贵的过程。在这样的应用中,测量需要在数千个点上进行。为了设计一个用于土壤湿度测量的传感器网络,我们介绍了两个步骤的设计过程;第一步,利用SMEX03土壤水分实验数据(位于俄克拉何马州的Little Washita流域)来近似水分数据的空间变异性。基于SMEX03的数值数据,对土壤湿度的空间相关性进行了近似计算。我们的数值分析表明,两点湿度测量的空间相关性表现为两点距离的指数衰减函数。分析还表明,距离150m以内的点的湿度测量值具有较高的相关性,而距离400m以上的点的空间相关性几乎为零。第二步,利用土壤湿度的空间相关性设计传感器网络。假设传感器稀疏地放置在田间,但希望根据附近传感器的测量值估计田间任意点的土壤湿度。我们使用线性估计器,并给出使其方差最小的系数。线性估计器的最小方差值取决于位置。我们给出了线性最小方差估计器的系数的封闭形式公式和其方差随传感器空间距离的函数的上界。假设最大允许水分估计方差的已知值,我们找到传感器的最佳位置。结果表明,在网格式的田间布置中,当相邻传感器对之间的平均间距为50 ~ 100m时,在田间任意点均能较好地逼近土壤湿度,而当相邻传感器对之间的距离超过200m时,其逼近精度会显著降低。
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Using field data to design a sensor network
Distributed sensing and data acquisition in field applications is a labor intensive and expensive process. In such applications, measurements need to be performed in thousands of points. To design a sensor network for soil moisture measurement, we introduce a two step design procedure; in the first step, the data of soil moisture experiments known as SMEX03 (in Little Washita watershed, Oklahoma) is used to approximate the spatial variability of moisture data. Based on the numerical data of SMEX03, the spatial correlation of soil moisture is approximated. Our numerical analysis shows that the spatial correlation of moisture measurements of two points behaves similar to an exponentially decaying function of the distance of those points. The analysis also shows that the moisture measurements for the points with distance up to 150m show a high correlation, while the spatial correlation is practically zero for points that are more than 400m apart. In the second step, we use the spatial correlation of soil moisture to design a sensor network. It is assumed that the sensors are placed sparsely in the field, but it is desirable to estimate the soil moisture at any arbitrary point of the field based on the measurements of the nearby sensors. We use a linear estimator, and give the coefficients that minimize its variance. The value of the minimum variance of the linear estimator depends on the location. We give a closed form formula for the coefficients of the linear minimum variance estimator and the upper bound for the its variance as a function of spatial separation of sensors. Assuming a known value for the maximum allowable moisture estimation variance, we find the optimal placement of the sensors. The results show that in a grid like placement of the sensors in the field, with average separation of distance of 50-100m between neighboring sensor pairs, the soil moisture can be approximated with a good accuracy at any arbitrary point of the field, while increasing the distance of neighboring senors beyond 200m degrades the performance significantly.
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