In-Situ Soil Moisture Sensing: Efficient Random Sensor Placement and Field Estimation Using Compressive Sensing

This paper presents the first complete design to apply Compressive Sensing (CS) theory to sensor placement and entire field estimation for in-situ soil moisture sensing. For such specified application, the measurements are usually spatially correlated, which can be sparsely represented under appropriate linear transformation. One scalable random placement algorithm with higher incoherence with the sparse-representation basis is proposed and the classical CS recovery algorithm is immediately exploited to obtain entire field's soil moisture value. Further- more, the compressibility is significantly improved by discovering one relative stationary monotonic non-decreasing coarse-grained ordering of locations in terms of soil moisture over time. Our numerical experiments show that random placement algorithm applying relative stationary coarse-grained ordering to re-label all locations can lead to the estimation performance improvement, compared with that leveraging initial ordering and the output of well calculated sub- optimal permutation algorithm. We further empirically prove that the performance of field estimation using CS recovery algorithm is much more robust to the previous knowledge about the field and is very suitable for the sensing application without enough historical data.