Vegetation Attenuation Measurements and Modeling in Plantations for Wireless Sensor Network Planning

Abstract

Abstract-As wireless communication moves from long to short ranges with considerably lower antenna heights, the need to understand and be able to predict the impact of vegetation on coverage and quality of wireless services has become very important. This paper focuses on vegetation attenuation measurements for frequencies in the range 0.4-7.2 GHz in mango and oil palm plantations to evaluate vegetation attenuation models for application in wireless sensor network planning and deployment in precision agriculture. Although a number of models have been proposed and evaluated for specific frequencies, results show that these models do not perform well when applied to different vegetation types or at different frequencies. A global assessment of the models using a broad range of frequencies shows that the COST 235 model gives more consistent results when there is vegetation in the propagation path. For grid-like plantation, the study shows that the RET model provides the best prediction of path loss for measurements between two rows of trees. However, taking into account the limited number of parameter values available for the RET model and the potential inaccuracy that may results from the use of a wrong parameter value, a sub-optimal model which combines the ITUR model with ground reflection does offer a more consistent prediction. The differences in the average values of RMS error between RET, ITUR and free space loss models when combined with ground reflection is less than 1.6 dB.

Design and Simulation of the Novel Wireless Sensor Network Spectrum Allocation Algorithm

“…After the sensor acquisition task, usually to send data to the information fusion and processing centers are extracted from a distance. Sensor network is not just a sense of the data acquisition and the transparent transmission, the network in real time and reliably transmit data at the same time, is also an information processing factories, on the basis of the original data collection, internal work for a large amount of the data processing, network transmission to the network of the external terminal will sensory information from the mass that is likely to be messy and difficult to understand the original data of extraction and derived for some specific user valuable and meaningful data after processing [4][5].…”

The Irregularity Propagation Characteristics of Radio Signals For Wireless Sensor Network In Farmland

“…To evaluate the reliability of models application, Root Mean Square Error (RMSE) between the models and the measured data are further calculated. The RMSE is a frequently used measure of the difference between values predicted by a model and the values actually measured from the environment [17]. RMSE is a good measure for accuracy of the models, and can be expressed by:…”

Rf Propagation Experiments in Agricultural Fields and Gardens for Wireless Sensor Communications

“…Free Space Loss (FSL) model [20] is represented for radio wave propagation as FSL = 20 log 10 (f ) + 20 log 10 (d) − 27.56 (1) where f is the frequency in MHz and d the distance in meters. And, in all wireless environments, the RSS power may be expressed [19] as…”

“…To assess the wider application of models, Root Mean Square Error (RMSE) [20] between the models and the observed data were calculated. The RMSE is a frequently used measure of the difference between values predicted by a model and the values actually observed from the environment.…”