# An Approach to Dynamic Sensing Data Fusion
## 1 Introduction  
Sensors are one of the main equipment for data acquisition and an important bridge to obtain environmental information. How to process the data collected by sensors is a hot research topic in industry. Because of the complex and changeable environment, sensors cannot perform their best. How to dynamically adjust the sensor acquisition frequency according to environmental changes and then fuse the sensor data is therefore very meaningful. Sensor data acquisition indicators include accuracy and bandwidth. The former requires the collected data to accurately reflect the objective facts, while the latter requires the collected data to be as few as possible in order to achieve the fastest transmission efficiency. At present, there are two kinds of sensor data acquisition methods in industry and academia: the best accuracy method and the minimum bandwidth method. In order to improve the accuracy of the best accuracy method, we usually adopt the method of shortening the acquisition time interval. The advantages of this method are high precision and accurate prediction, while the disadvantages are larger transmission bandwidth occupation and lower handling efficiency. The sensor data collection of DSP chip TMS320 VC33 [1] and the sensor data collection based on the Advantech card PCI-1811L [2] are examples of the best accuracy method. In the minimum bandwidth method, in order to reduce the number of acquisitions, we usually expand the acquisition time interval. Therefore, the minimum bandwidth method reduces the number of acquisitions and achieves the real-time processing requirements for environmental data. The advantage of this method is high collection efficiency that is suitable for concurrency and real-time systems, while the disadvantage is lower collection precision. The multiplex signal asynchronous sampling model proposed by [PERSON] et al. [3], microbial environmental data collection [4], data collection in biological and environmental sciences [5], and collection of experimental data for aerosol monitoring cyclones [6] belong to this sensor data acquisition method. In practical applications, the best accuracy method and the minimum bandwidth method are often combined. The merged method is a compromise method, which uses as little bandwidth as possible to meet the basic acquisition accuracy, thus promoting the progress of environmental data acquisition [7; 8; 9]. However, the traditional method is a static method, which does not take into account the dynamic characteristics of current environmental data. If we can mine the changes in sensor data from the current collected data in the recent period, then we can regard them as a priori knowledge to dynamically adjust the collection intervals and therefore achieve collection precision and maximum utilization of transmission bandwidth. Based on this, we adopted a dynamic collection method for sensor data that predicts the changing degree of data according to the collected sensor data and decides whether the current acquisition timeintervals need to be dynamically adjusted. If the timeintervals need to be adjusted, then the algorithm will be immediately conducted; otherwise, the original values are maintained. The method proposed in this paper can achieve collection precision and maximum utilization of transmission bandwidth. In this paper, we propose a dynamic and adaptive data acquisition interval adjustment method to solve the problem of data acquisition efficiency and accuracy. The key issues are judgment of data changing degree and dynamic regulation of acquisition timeintervals. The judgment process of data changing degree is as follows. First, collect N data and compute the predicted value of the N+1 data based on the collected N data. Second, collect the N+1 data and compute its measured value. Finally, compare the predicted value and the measured value of the N+1 data.If the difference between the predicted value and the measured value of the N+1 data exceeds the designated threshold, the acquisition timeintervals need to be adjusted.