Research On Data Gathering Technology Based On Matrix Completion For Wireless Sensor Networks

Wireless Sensor Networks(WSNs)use a variety of sensor nodes to monitor the target area and collect various kinds of information in real time.Then they send the information to the sink node wirelessly.WSNs have vast application prospects in many fields,such as environmental surveillance,m-health,traffic monitoring.Data gathering is a basic task of wireless sensor networks,for which the issue of energy constraint is a major challenge.To improve the energy efficiency of the network,it is an important method to utilize inherent correlation of sensing data to reduce the amount of data gathered and transmitted.This thesis focuses on the data gathering scheme for WSNs based on matrix completion.First,based on node scheduling technology,a Data Gathering scheme based on Residual energy and matrix Completion(DGRC)is proposed.At the sensor node,based on nodes' residual energy,DGRC schedules nodes with the lowest energy into dormant mode,to balance the energy consumption of the network.At the sink node,DGRC uses a matrix completion algorithm to recover the whole sensing data,to ensure the reliability of the data.Based on DGRC,an adaptive filtering algorithm is introduced,and a Data Gathering scheme based on adaptive Filtering and matrix Completion(DGFC)is proposed.At the sensor node,DGFC uses an adaptive filtering algorithm to predict and filter the data.The real sensing data is transmitted only when the difference between it and the prediction result exceeds a certain range.Therefore,the sensing nodes do not need to submit all the sensing data.At the sink node,DGFC also performs a matrix completion algorithm on the received data to recover all readings.Simulation experiments show that both of the schemes reduce the redundant data in communication,balance the energy consumption among nodes,prolong the lifetime of the network,and ensure the high accuracy of the data.