期刊
JOURNAL OF SENSORS
卷 2021, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2021/5591187
关键词
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资金
- Natural Science Foundation of School of Computer Science and Information Engineering, Harbin Normal University [JKYKYZ202102]
- Harbin Normal University pH.D. Research Start-up Gold Project [XKB201905]
- postgraduate practice innovation project of School of Computer Science and Information Engineering, Harbin Normal University [HSDSSCX2020-57]
- Innovation and Entrepreneurship Project of college students in Heilongjiang Province [202010231009]
This paper focuses on the impact of disturbance factors on the reliability evaluation of wireless sensor network data, using temporal correlation and spatial correlation as reliability indicators, proposing a model based on the evidence reasoning rule to analyze data reliability.
Wireless Sensor Network (WSN) is widely used in precision engineering, which requires strict data accuracy. Therefore, it is of practical value to evaluate the reliability of WSN data. Considering the complexity of the real environment, the sensor is bound to be affected by the disturbance factors. Currently, the research result of WSN data disturbance is not ideal. Because the results of reliability analysis are not necessarily credible under a disturbance environment. Thus, it is necessary to judge the reliability of sensor nodes in the disturbance environment. Therefore, disturbance analysis is introduced. In this paper, the temporal correlation and spatial correlation of measured data of WSN nodes are taken as reliability indicators. Through the disturbance analysis method to simulate the disturbance in the working process of nodes, a data reliability evaluation model of WSN nodes is proposed, which is based on the evidence reasoning (ER) rule in the disturbance environment. Based on the wireless sensor experiment of the Intel Berkeley research laboratory, the rationality of the model is explained in this paper. ER rule considering disturbance provides an effective method to analyze the reliability of WSN data.
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