Spatio-temporal soil moisture measurement with wireless underground sensor networks

In this paper, the estimation distortion of distributed soil moisture measurement using Wireless Underground Sensor Networks (WUSNs) is investigated. The main focus of this paper is to analyze the impact of the environment and network parameters on the estimation distortion of the soil moisture. More specifically, the effects of rainfall, soil porosity, and vegetation root zone are investigated by exploiting a rainfall model, in addition to the effects of sampling rate, network topology, and measurement signal noise ratio. Spatio-temporal correlation is characterized to develop a measurement distortion model with respect to these factors. The evaluations reveal that with porous soil and shallow vegetation roots, high sampling rate is required for sufficient accuracy. In addition, the impact of rainfall on the estimation distortion has also been investigated. In a storm, which carries on a large area and lasts for a long time, the estimation distortion is decreased because of the increase in spatial correlation. Moreover, only few closest sensors are needed to estimate the values of an interested location. These findings are utilized to guide the design of WUSNs for soil moisture measurement to reduce the density of network and the sampling rate of the sensors but at the same time maintain the performance of the system. Moreover, guidelines for designing WUSNs for soil moisture measurement are provided. To the best of our knowledge, this is the first work that establishes tight relations between environmental effects and distributed measurement in WUSNs.