Temperature error correction based on BP neural network in meteorological wireless sensor network

Abstract

Using meteorological wireless sensor network (WSN) to monitor the air temperature (AT) can greatly reduce the costs of monitoring. And it has the characteristics of easy deployment and high mobility. But low cost sensor is easily affected by external environment, often leading to inaccurate measurements. Previous research has shown that there is a close relationship between AT and solar radiation (SR). Therefore, we designed a back propagation (BP) neural network model using SR as the input parameter to establish the relationship between SR and AT error (ATE) with all the data in May. Then we used the trained BP model to correct the errors in other months. We evaluated the performance on the datasets in previous research and then compared the maximum absolute error, mean absolute error and standard deviation respectively. The experimental results show that our method achieves competitive performance. It proves that BP neural network is very suitable for solving this problem due to its powerful functions of non-linear fitting.