Prediction of Forest Temperature Microchange by Wireless Sensor Network Technology

ABSTRACT- The forest temperature microchange becomes critically important due to the study of global change. In this paper we develop a novel model to predict the measured data for forest temperature microchange by the wireless sensor network technology. The measurement system includes zigbee and Tmote Sky wireless sensor network system. The model is developed by the temperature second-order differential equation. From the measurement and model analysis, we found the temperature reaches maximum peak at different time due to the different dielectric constant of tree skin. The time difference is in the range of 1 to 2 hours. In model development we also found the distribution of temperature microchange has an exponential function distribution. After comparisons of model prediction with the measured data the correlation coefficient between them is 0.935736. The excellent prediction reaches in this model study.