Creditability Analysis of Sensor Data in the Cyber-physical System Based on the Relationship Diagram Model
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摘要: 针对信息物理融合系统(CPS)感知层数据的不确定性与随机性,该文提出一种CPS中感知数据的可信性分析框架。摒弃以往以传感器为中心的建模思路,该文充分考虑被监测对象因素,建立传感器-目标关联图模型,以此为基础设计了传感数据可信性推理算法。同时,为提高算法的实时性,减少传感器-目标关联图的搜索空间与时间,设计了基于可信目标筛选的改进推理算法。通过实例验证表明,该算法能实时、有效地滤掉CPS中感知数据中的虚假信息,极大提高感知数据的可信性。Abstract: The high uncertainty and randomness are the characteristics of the sensor data in the Cyber-Physical Systems (CPS), which make the data unreliable. A creditability analysis framework is proposed to solve those problems. Abandoning the idea that the sensor is the center in modeling, the theory takes monitoring targets into consideration and constructs the sensor-target relationship diagram, which is the base of the creditability reasoning algorithm. Meanwhile, in order to reduce the space and time of searching the relationship diagram, an improving reasoning method basing on filtering the incredible targets is designed. The examples demonstrate that the proposed algorithm can filter out the false message in the sensor data and enhances the creditability of the data in CPS.
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Key words:
- Cyber-Physical System (CPS) /
- Sensor data /
- Creditability analysis /
- Relationship diagram
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