Research On WSN Adaptive Data Modeling Method

Wireless Sensor Networks(WSN),as a fusion of sensor technology and wireless communication technology,has penetrated into all aspects of human life,but while WSN brings convenience to people,it also has its own limitations.First,the data collected by the sensor node has great similarity between the adjacent time zone and the adjacent space domain.This results in the problems of high data redundancy,large amount of data transmission,and large network energy consumption;Secondly,the processing resources of the WSN are affected.The sensor node is powered by a dry battery,and it is difficult to replace the battery and cannot obtain energy.Therefore,in the face of massive incentive input and resource-constrained situations,how to ensure the accuracy of data while improving the efficiency of resource use to reduce the network energy consumption is the focus of this study.The main work of this paper is as follows:First,the data fusion model and related data fusion algorithms are described in detail.For the large-scale,high-complexity and limited processing resource constraints faced by WSN,this paper uses the attention mechanism of the human intelligent perception system to analyze the top-level of attention.The characteristics of the bottom-up and bottom-up information,and how to organically combine the information in the wireless sensor network to improve the system's ability to handle significant information.Secondly,in order to solve the problem of high data redundancy of WSN nodes,the paper proposes a WSN data fusion algorithm based on regression model prediction.By analyzing the perceived time series within the sliding window,a prediction model is established.For data fluctuations at different times,a sliding window mechanism was introduced to segment the model to better fit the sensor data.According to model parameters and given error detection model validity,the prediction model is dynamically updated.The algorithm reduces data redundancy and network energy consumption,and improves data processing efficiency.Third,by learning and simulating the attention mechanism in the human intelligence perception system,the fourth chapter of the thesis proposes a attention mechanism based on Bayesian frame for model updating.The information of the forecasting model in the third chapter is used as bottom-up information for abnormal event detection.At the same time,top-down information related to user needs is added to improve the efficiency of data expression.The attention mechanism is used to estimate the significance of the time series.According to the salience,dynamically adjusts the error threshold to change the model accuracy.This method analyzes the user's demand and dynamically updates the model according to the importance of the data.It ensures the timely delivery of important information and effectively reduces the amount of data sent,thus achieving elasticity perception of the scene under conditions of restricted processing resources.Finally,for the problem that the sensing node directly transmits data to the Sink node and consumes more energy and the redundancy of the data collected by neighboring nodes is high,the LEACH routing protocol is improved on the basis of the method in the fourth chapter.Firstly,the data collected by the member nodes in the cluster are eliminated by using the PauTa criterion;Then,at the head node of the cluster,the data from the cluster member nodes are adaptive weight fused,and the attention data is used to process the fused data.Establish an effective forecasting model and dynamically adjust the accuracy of the model according to the importance of the data.Finally,the cluster head node forwards the model update parameters to the Sink node.This method can effectively extend the network life cycle under the premise of satisfying certain data accuracy.