Study Of Data Acquisition And Prediction Of Wireless Sensor Network Based On Intelligent Algorithm

As a data acquisition network.Wireless sensor network(WSN)deploys a large number of miniature wireless sensor nodes in the target area to help people obtain information about the target object and the surrounding environment of the target,thereby achieving the interconnection between people and things,things and things.However,sensor nodes are constrained by size and cost,usually powered by batteries,and due to various comprehensive factors,the batteries are not easy to replace.The constraint of energy brings a significant challenge to the lifetime of WSN.How to use node energy efficiently to maximize the network lifetime is a hot issue in the research of WSN.In addition,how to mine and analyze the sensor network data obtained by users to avoid the problem of "rich data and poor knowledge" is another important issue in the research of WSN.In view of the problems existing in the data acquisition and analysis stage of WSN,this paper focuses on how to ensure that the WSN collects data for a long time and how to analyze the sampled data.The main research contents include the following two aspects:In the stage of data acquisition,it was found that most of the energy of the sensor nodes is consumed during the wireless communication between the nodes,and the efficient routing algorithm can plan a more reasonable communication link,thereby improving the node energy utilization and ensuring the sensors.In this regard,this paper has studied two aspects.First,a clustering routing algorithm for WSN based on a static base station structure is proposed.The improved particle swarm(PSO)algorithm is used to elect the cluster head.Ordinary nodes select the cluster head according to the weight function and join the cluster.The improved ant colony optimization(ACO)algorithm is used to plan the multi-hop transmission path within and between the clusters.Second,based on the foregoing research,a clustering routing algorithm for WSN based on a dynamic base station structure is proposed.The base station equipment is mounted on the drone to build a dynamic base station.Using the improved PSO to cluster the nodes deployed on the ground approximately uniformly,using the ACO algorithm to plan the drone flight path,and also select "redundant nodes" for the cluster head nodes to improve the robustness of cluster head data transmission.In the stage of data analysis,in order to improve the accuracy and robustness of prediction,this paper proposes a personalized online ensemble learning method to predictthe sensor network data stream.According to the time sequence of the data stream,the prior data is divided into several data blocks,and a predictor is trained for each data block.After receiving a new data block at the current moment,the user calculates the relative entropy value between this data block and several data blocks closest to it,to calculate the similarity between its distribution with each data block,and based on these The value determines the prediction weight of each predictor,thereby generating a strong prediction model.