A Self-adjusting Parametric Model for Attenuation Characteristics of WUSN Signal

Abstract

Wireless Underground Sensor Network (WUSN) is gradually applied to smart agriculture for soil information collection and monitoring of crop growth environment. WUSN can avoid the inconvenience caused by tillage and other machine operation activities on farmland, and obtains multi-level and multi-dimensional parameters in the underground soil environment, which is crucial for soil moisture monitoring of crops. However, WUSN has no universally applicable transmission protocol standards in the field. Therefore, the research of different soil compositions on the placement of wireless sensor network nodes can provide scientific guidance to obtain soil moisture information of agricultural fields, it is important for the development of precision agriculture. In this paper, a low-power WUSN nodes was designed, based on modified Frisian transmission model and the complex refractive index Fresnel model, we proposed an adaptive optimization model, and also proposed an improved Genetic Algorithm, which is automatically adjust fusion parameter according to soil and distance factors, it made the prediction of signal attenuation under different soil components more accurately. We used the adaptive optimized model for signal prediction, comparing with the modified Friis prediction model and the complex refractive index Fresnel prediction model, the results shown that the proposed adaptive optimization model with automatic parameter is convenient to predict the signal attenuation, the adaptive optimization model made the prediction error stay really low. In order to compare with other sensors in the soil environment, the temperature of the distributed fiber optic temperature sensor was tested, which predicted by the adaptive model. The result shown that the adaptive model is more favorable to the prediction of signal attenuation in WUSN than distributed fiber optic temperature sensors.