Credibility assessment method of sensor data based on multi-source heterogeneous information fusion

Abstract

The credibility of sensor data is essential for security monitoring. High-credibility data are the precondition for utilizing data and data analysis, but the existing data credibility evaluation methods rarely consider the spatio-temporal relationship between data sources, which usually leads to low accuracy and low flexibility. In order to solve this problem, a new credibility evaluation method is proposed in this article, which includes two factors: the spatio-temporal relationship between data sources and the temporal correlation between time series data. First, the spatio-temporal relationship was used to obtain the credibility of data sources. Then, the combined credibility of data was calculated based on the autoregressive integrated moving average (ARIMA) model and back propagation (BP) neural network. Finally, the comprehensive data reliability for evaluating data quality can be acquired based on the credibility of data sources and combined data credibility. The experimental results show the effectiveness of the proposed method.