Research Of Data Collection Algorithm Based On Compressive Sensing In Wireless Sensor Networks

The Wireless Sensor Network(WSN)is composed of a large number of sensing nodes randomly distributed in the monitoring environment,which plays an indispensable role in various fields such as agriculture,military,environment,and industry.The nodes collect and transmit data to base station(BS)through sensing module,collection module and transmission module.Then the BS completes data analysis and processing.Due to the harsh environment of WSN deployment,it is difficult to reach by human,and the cost of nodes' energy supplement and replacement is high.Therefore,the energy consumption,network balance,stability and network life cycle during data collection are the foci of research.The energy of WSN is mainly used for data receiving and sending,so reducing the amount of data transmission is one of the main ways to reduce energy consumption.Using the sparsity of signals,Compressive Sensing(CS)can reduce high-dimensional signals to lowdimensional signals through the measurement matrix,which can effectively reduce the amount of data transmission,and the original data can be reconstructed by reconstruction algorithms.This thesis combines CS theory in the process of data collection to efficiently compress data,reduce the amount of data transmitted and received,and achieve the purpose of reducing network energy consumption and prolonging the network life cycle.The specific research work is as follows:(1)Aiming at the problems of dense deployment of nodes and high data redundancy in WSN,this thesis reduces the nodes participating in sampling by integrating CS theory and spatial correlation to improve the network transmission efficiency.In view of the strong compressibility of the data collected by the nodes in a short period of time,this thesis compresses the data of multiple time slots by CS theory and temporal correlation,so as to reduce the amount of data received and sent.Therefore,by combining the CS theory with the spatiotemporal correlation,the purpose of reducing node energy consumption and extending the effective working time of the network can be achieved.(2)Aiming at the problems of high energy consumption,poor balance and large number of nodes deployed in the existing chained collection algorithms,this thesis proposes a chained data collection algorithm based on space-time compression.Firstly,the algorithm combines the space-time correlation of the network based on the Random Walk(RW)algorithm to fully compress the data collected by the nodes;Secondly,during the start of the walk,the algorithm uses the proposed node category to start the data collection process;Then,the algorithm predicts the length of the transmission and judges the direction of the walk according to the distribution of the neighbor nodes,so as to improve the problem of the high energy consumption caused by the long transmission length in RW;In order to alleviate the uneven energy consumption of the node,the algorithm balances node's accessing quantity through the access record mechanism;Finally,the feasibility of the algorithm is verified through simulation,and results show that compared with the existing CS-RW,STCS-RW and STCDG-TW algorithms,the proposed algorithm reduces the node energy consumption,improves network balance,and saves the number of nodes deployed.(3)In order to further improve the balance performance of the network and prolong the life of the network,this thesis proposes a clustering data collection algorithm based on Fuzzy ART.Firstly,according to the relationship between spatial correlation and cluster,combining the Fuzzy ART,Hausdorff distance and data dissimilarity,the proposed algorithm optimizes the cluster formation process.Then,the algorithm designs a new cluster head rotation mechanism according to the residual energy of nodes,the distance of intra-communication and inter-communication;Secondly,in order to improve the collection efficiency of the cluster,the algorithm improves the selection strategy of functional nodes;In addition,the influence of routing on network energy consumption and stability is studied to establish intra-cluster two hops routing and inter-cluster staged routing;Finally,the effectiveness of the algorithm is verified through simulation.Compared with the existing Cluster HCS algorithm,the proposed algorithm can effectively reduce network energy consumption,improve network balance and stability,and further extend the network life cycle compared with the chained data collection algorithm based on space-time compression proposed in this thesis.