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

Authors: Baowei Wang; Xiaodu Gu; Li Ma; Shuangshuang Yan

Addresses: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing 210044, China; Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China; Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

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.

Keywords: WSN; wireless sensor network; data correction; artificial neural network; solar radiation.

DOI: 10.1504/IJSNET.2017.083532

International Journal of Sensor Networks, 2017 Vol.23 No.4, pp.265 - 278

Received: 28 Aug 2016
Accepted: 13 Oct 2016

Published online: 09 Apr 2017 *

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