Forecasting the Energy Utilization in WSN by Support Vector Regression Model

Energy management issues in Wireless Sensor Networks (WSNs) are an important research topic because they are widely used to monitor a variety of physical conditions. Though several analytical methods are available, it is still challenging to make a precise model to forecast energy utilization due to the prompting parameters' difficulty. It is critical for an energy manager to accurately predict energy consumption in order to make sound decisions and achieve energy utilization. The dimensionality reduction and efficient feature selection process in WSN is challenging. In many real-time situations, machine learning methods such as Support Vector Regression (SVR) provide greater accuracy. This paper proposes a Support Vector Regression (SVR) model for forecasting the processing energy utilization in WSN. The SVR system maps the input variables to high dimensional feature space and discovers the nonlinear connection between the input and output by reducing the data load. Furthermore, the Boruta algorithm is used to select the feature and improve the forecasting accuracy. The experimental outcomes demonstrate the FSVR method enhances the accuracy and minimizes the processing energy utilization in the WSN.