# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 3 System Model and System Architecture
### Data Fusion Module  
The data fusion module is responsible for preprocessing sensor data by cleaning up invalid binary data collected by sensors and integrating results from different sensors. Multisensor information fusion technology is suitable for processing multimodal data formats, enabling the fusion of different levels and forms of data information.  
Multiple sensor information fusion (MSIF), also known as information fusion technology, initially had its roots in military applications. However, with the continuous improvement and upgrade of industrial systems, this technology has been extended to the civil field for use in medical diagnosis, mechanical fault diagnosis, air traffic control, remote sensing, intelligent manufacturing, intelligent transportation, industrial intelligent control, and criminal investigation. As a frontier technology, both military and civil systems are inclined to use data fusion technology for comprehensive information processing. In the  
Figure 4: FM spectrum of monotonic modulation.  
Figure 3: AM spectrum of monotonic modulation.  
era of knowledge explosion, data fusion technology plays a critical role as it helps to avoid the issue of rich data but poor information.  
Data fusion is a multilevel automatic information processing process that utilizes information from various sources, modes, times, places, and expressions to obtain an accurate description of the perception object. Multisensor data fusion involves using different sensor observation information to automatically analyze and synthesize the observation information of multiple sensors obtained over time through a certain method to obtain more effective information.  
This data collection model is based on the data layer fusion of multisensor information fusion technology. It involves collecting data from multiple sources, detecting and analyzing the collected data signals, and then fusing the data from multiple sensors. The Bayesian solution is used for data fusion, as shown in Figure 5, which requires that both sensors have available information [38].  
The probability density function can be expressed as  
\[p\big{(}x(t)\mid Y_{1}^{t},Y_{2}^{t}\big{)}= \tag{10}\] \[p(y_{1}(t)\mid x(t))p(y_{2}(t)\mid x(t))\frac{p\Big{(}x(t)\mid Y_ {1}^{t-1},Y_{2}^{t-1}\Big{)}}{p\Big{(}y_{1}(t),y_{2}(t)\mid Y_{1}^{t-1},Y_{2}^ {t-1}\Big{)}}\]  
where \(p(y_{1}(t)\mid x(t))\) and \(p(y_{2}(t)\mid x(t))\), respectively, represent the available information of the two sensors. \(p\big{(}x(t)\mid Y_{1}^{t},Y_{2}^{t}\big{)}\) represents the result of the data fusion of two sensors at time \(t\). \(p\Big{(}y_{1}(t),y_{2}(t)\mid Y_{1}^{t-1},Y_{2}^{t-1}\Big{)}\) represents the joint likelihood function of two independent sensors. The joint likelihood function of two independent sensors is the product of the likelihood functions of each sensor.  
The most crucial step for fusion is to calculate the scores based on the data collected by the sensor  
\[\frac{p\big{(}x(t)\mid Y_{1}^{t}\big{)}}{p\Big{(}x(t)\mid Y_{1}^{t-1}\Big{)}} \text{ and }\frac{p\big{(}x(t)\mid Y_{2}^{t}\big{)}}{p\Big{(}x(t)\mid Y_{2}^{t-1}\Big{)}} \tag{11}\]  
Data layer fusion provides a wealth of accurate data and enables the easy extraction of data details without losing any information. It is essential to effectively and intelligently fuse the outputs of sensors to achieve optimal performance.