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Credibility Assessment Method of Sensor Data Based on Multi-Source Heterogeneous Information Fusion.
Yanling Feng1, Jixiong Hu1, Rui Duan1
1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|April 30, 2021
Summary
This study introduces a novel sensor data credibility evaluation method. It enhances security monitoring by considering spatio-temporal relationships and temporal correlations for accurate data reliability assessment.
Area of Science:
- Computer Science
- Data Science
- Sensor Networks
Background:
- Sensor data credibility is vital for security monitoring and data analysis.
- Existing methods lack consideration of spatio-temporal relationships, leading to inaccuracies.
Purpose of the Study:
- To propose a new method for evaluating sensor data credibility.
- To improve accuracy and flexibility in data reliability assessment.
Main Methods:
- Incorporating spatio-temporal relationships between data sources.
- Utilizing autoregressive integrated moving average (ARIMA) and back propagation (BP) neural networks for combined credibility.
- Calculating comprehensive data reliability.
Main Results:
- The proposed method effectively evaluates sensor data credibility.
- Experimental results demonstrate the method's effectiveness.
Conclusions:
- The new method enhances data quality assessment for security monitoring.
- Considering spatio-temporal and temporal correlations improves credibility evaluation.
