Data Aggregation With Principal Component Analysis In Wireless Sensor Networks

In the wireless sensor networks(WSNs),a large number of different types of sensor nodes are generally deployed in order to monitor sensing areas in real-time and collect information accurately and comprehensively,so the sensing data in WSNs has characteristics of volume,variety and velocity.In addition,since the wireless sensor nodes are randomly distributed in the sensing area,sense areas of most nodes are overlapped in the network,which directly results in sensing data redundancy.Moreover,since the energy of the wireless sensor nodes comes from limited micro batteries,the transmission of redundant data can cause extra energy consumption.Therefore,it is very important to eliminate data redundancy in the process of transmission to reduce the extra energy consumption.Data aggregation is an effective method to eliminate data redundancy.Therefore,in order to solve the problem of high energy consumption of data redundancy in WSNs,this thesis mainly takes PCA to eliminate data with high aggregation degree in the cluster and improve energy efficiency.The main contents of this paper are as follows:Aiming at the redundancy problem of data transmission in WSNs,this thesis presents an algorithm based on data similarity.The theory is that the more similar data sets are,the smaller the amount of data after aggregation is.Specifically,a new clustering method is first proposed to divide the nodes with high data similarity into a cluster to further improve the data aggregation degree.Then,in order to avoid the transmission conflict of nodes,the energy model to limit the number of nodes in a cluster is proposed,which achieves a balance of energy consumption in the cluster.Finally,in order to eliminate the maximum data redundancy in the transmission,this thesis adopts the data aggregation algorithm based on principal component analysis in each cluster head,which ultimately reduces the transmission of redundant data and the energy consumption.The simulation results show that our proposed algorithms can effectively reduce the data transmission by 21.1% and 13.4% compared with LEACH-PCA and K means-PCA algorithm,respectively,and network survival time can be effectively extended by 7.8%.When the number of nodes is more than 300,the energy consumption of our algorithm is significantly lower than other algorithms.Therefore,it is proved that both data aggregation algorithm and clustering algorithm proposed in this thesis improve the network performance.