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首页> 外文期刊>ACM transactions on sensor networks >In-situ Soil Moisture Sensing: Measurement Scheduling and Estimation Using Sparse Sampling
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In-situ Soil Moisture Sensing: Measurement Scheduling and Estimation Using Sparse Sampling

机译：原位土壤湿度传感：使用稀疏采样的测量计划和估算

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We consider the problem of monitoring soil moisture evolution using a wireless network of in-situ underground sensors. To reduce cost and prolong lifetime, it is highly desirable to rely on fewer measurements and estimate with higher accuracy the original signal (the temporal evolution of soil moisture). In this article, we explore the use of results from the theory of sparse sampling, including Compressive Sensing (CS) and Matrix Completion (MC), in this application context. We first consider the problem of reconstructing the soil moisture process at a single location using CS. Our physical constraint leads to very sparse measurement matrices, which makes finding a suitable representation basis very challenging: it needs to make the underlying signal sufficiently sparse while at the same time being sufficiently incoherent with the measurement matrix, two common preconditions for CS techniques to work well. We construct a representation basis by exploiting unique features of soil moisture evolution and show that this basis attains a very good tradeoff between its ability to sparsify the signal and its incoherence with measurement matrices that are consistent with our physical constraints. We next consider the problem of jointly reconstructing soil moisture processes at multiple locations, assuming sparse measurements can be taken at each location. We show that the spatial soil moisture process enjoys a low-rank property, a priority for MC. Accordingly, we introduce a spatiotemporal measurement matrix and apply the MC framework to reconstruct the soil moisture field. Extensive numerical evaluation is performed on both real, high-resolution soil moisture data and simulated data and through comparison with a closed-loop scheduling approach. Our results demonstrate that, for a single location, a uniform measurement scheduling followed by CS recovery results in a very nice tradeoff between estimation accuracy, sampling rate, flexibility, and feasibility in implementation. When multiple locations are available, our results show that joint reconstruction using MC in general produces better estimation accuracy than using a single location alone, but it requires the use of independent and random measurement schedules across locations. We also show that these sparse sampling techniques can be augmented so as to be robust against sporadic data outliers/corruption caused by, for example, intermittent sensor faults.
机译：我们考虑使用现场地下传感器的无线网络监测土壤水分释放的问题。为了降低成本并延长使用寿命，非常需要依靠较少的测量并以较高的精度估算原始信号（土壤水分的时间演变）。在本文中，我们探索了在此应用程序上下文中稀疏采样理论（包括压缩感测（CS）和矩阵完成（MC））的结果的使用。我们首先考虑使用CS在单个位置重建土壤水分过程的问题。我们的物理约束导致测量矩阵非常稀疏，这使得寻找合适的表示基础变得非常困难：它需要使基础信号充分稀疏，同时又与测量矩阵充分不相干，这是CS技术工作的两个常见前提好。我们通过利用土壤水分演化的独特特征构建了一个表示基础，并表明该基础在信号稀疏化能力和与我们的物理约束一致的测量矩阵的不一致性之间取得了很好的折衷。接下来，假设可以在每个位置进行稀疏测量，那么我们将考虑在多个位置共同重建土壤水分过程的问题。我们表明，空间土壤水分过程享有低等级的特性，这是MC的优先考虑。因此，我们引入了一个时空测量矩阵，并应用MC框架来重建土壤水分场。通过与闭环调度方法进行比较，可以对真实的高分辨率土壤水分数据和模拟数据进行广泛的数值评估。我们的结果表明，对于单个位置，统一的测量调度以及CS恢复可在估计精度，采样率，灵活性和实施可行性之间取得很好的折衷。当有多个位置可用时，我们的结果表明，与单独使用单个位置相比，使用MC进行的联合重建通常会产生更好的估计精度，但需要跨位置使用独立和随机的测量计划。我们还表明，可以增强这些稀疏采样技术，以便对因间歇性传感器故障引起的零星数据异常值/损坏具有鲁棒性。

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