Research On The Data Collection Method In Clustering Network Based On Hybrid Compressive Sensing

Nowadays,wireless sensor technology is integrated into our lives,learning,work,and entertainment.And low-power,low-latency data transmission in wireless sensor networks has been a hotspot.Node data acquisition and compression,efficient routing strategy,data processing,are all including in low-power network analysis.In this paper,we study the data collection method of traditional clustered wireless sensor networks and the clustering network data collection method based on hybrid compressive sensing.By studying the data collection algorithm of existing clustered wireless sensor networks,it is noted that the data transmission delay and throughput in the network are the key indexes to measure the quality of the protocol.Therefore,this paper proposes a data collection method based on the Markov model to select the mobile sink path.Firstly,the area of the wireless sensor network is divided,and then the Markov model is used to predict the position of the nodes.Secondly,the priority of the network area is given according to the predicted number of nodes.Then,the data is fused to each region,and finally the optimal mobile sink node motion trajectory.The method can be applied to networks of different sizes,and has the advantages of saving network energy,extending network life cycle,reducing overall network delay and preventing data overflow.Secondly,a data collection method of clustering sensor network based on BP neural network is proposed.Firstly,the wireless sensor network is monitored,and the geocentric location of the network is searched according to the GPS information of the node.Secondly,the cluster head node is selected according to the location information of the node and the cluster is constructed.Then,the BP neural network Model,and finally adjusts the number of clusters in the network according to the total number of hops of the network,and reaches the steady state of the optimal number of clusters.The method can be applied to different sizes of networks,collecting network data with the optimal number of clusters,and has the advantages of reducing network energy consumption,extending network life cycle and reducing network delay.In order to reduce the number of transmissions and balance the network load in wireless sensor network,this paper presents a data collection method by using hybrid compressive sensing in clustering network.Firstly choose some of the nodes that close to the temporary cluster-centroid as the candidate cluster head(CH),secondly determine the CH nodes on the basis of the distance of the candidate nodes to determined CH orderly,then the common sensor nodes join their nearest cluster,lastly build a data transmission tree root of Sink node that connect to all CHs greedy.When the number of data transmissions is higher than the threshold,nodes transmit data by using CS.On scenarios of compressive ratio equals 10,the simulation results demonstrate that the number of transmissions for the proposed method is 75% and 65% less than that of Clustering without CS and SPT without CS,35% and 20% less than that of SPT with Hybrid CS and Clustering with Hybrid CS;The standard deviation of nodes transmissions for the proposed method is 62% and 81% less than that of Clustering without CS and SPT without CS,41% and 19% less than that of SPT with Hybrid CS and Clustering with Hybrid CS.