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.
Highlights
Wireless sensor networks (WSN) have become popular in various areas [1,2,3,4]
When the data of multiple sensors are interfered, this method will not be able to make an effective evaluation of the data, so the event data and unreliable data cannot be handled
This paper proposes a new comprehensive credibility of data measurement (CDM) method, which combines the correlation between data sources and the fusion algorithm
Summary
Wireless sensor networks (WSN) have become popular in various areas [1,2,3,4]. For example, in agricultural production, WSN are used to monitor the growth environment and status of crops; in terms of industrial safety, WSN are applied to monitor the safety of dangerous working environments such as coal mines, oil drilling, and nuclear power plants to ensure the safety of workers. Due to the interference of many external factors such as sensor aging, instrument failure, measurement methods, and human interference, the data collected by sensors may not be precise or reliable [5]. The existence of these unreliable data will lead to inefficient data utilization, waste of economic costs, and even serious decision-making errors. When the data of multiple sensors are interfered, this method will not be able to make an effective evaluation of the data, so the event data and unreliable data cannot be handled
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