Research On Data Gathering Based On Compressive Sensing Theory In Wireless Sensor Networks

The continuous progress in wireless communication,sensor technology,micro-electro mechanical systems and other technologies has contributed to the development of wireless sensor networks.Sensor nodes of the wireless sensor network can be widely deployed in a particular area because of the advantages,such as cheap price,especially in harsh environment where people cannot reach.Wireless sensor networks can form a multi-hop and self-organizing network system to obtain external information through wireless communication,so it can be widely used.But the problems of weak storage,computing and energy supply capacities of sensor nodes have limited its application in real life.As a hot research frontier in recent years,compressive sensing has good compression performance,by which the data can be compressed and sampled simultaneously.Compressive sensing provides a good platform for the development of data collection of the wireless sensor network.We can collect sensing data in wireless sensor networks combined with compressive sensing theory,it is a new research hotspot in recent years.In this thesis,we focus on the methods of data collection using compressive sensing in wireless sensor networks and the data reconstruction based on the original compressive sensing methods.Firstly,by consulting a large number of documents on compressive sensing theory and its application in wireless sensor networks,the existing methods of compressive sensing are analyzed in details.Secondly,the sensing data collected by wireless sensor networks is constantly varying with change of time and space.But many methods of the existing compressive sensing with the fixed sample rate cannot capture these changes of the target signal.To solve the problem,an adaptive compressive sensing data collection strategy in wireless sensor networks is proposed,which can achieve a higher recovery accuracy with a lower sample rate.The implementation of the collection strategy can be divided into two phases:data training phase and testing phase.We can use the S-S diagram generated in the data training phase to match the number of measurements in different test windows in the testing phase adaptively.Moreover,a new sparse method is proposed to process data and the scheduling matrix is optimized simultaneously.The simulation results show that the collection strategy proposed in this thesis has good performance.Thirdly,a step-size regularized backtracking adaptive pursuit algorithm is proposed in the process of data reconstruction of compressive sensing for the situation of unknown sparsity and the fixed step size.Firstly,the sparsity of the signal is obtained by the adaptive method and the estimated value is used as the initial support set length of the next process.Then we combine the regularization method with the subspace tracking algorithm to achieve the second screening and remove the atoms which are not appropriate,also the variable step size is added to select atoms in the candidate set so that we can complete the signal reconstruction.The simulation results show that the proposed algorithm is superior to other algorithms in speed and reconstruction accuracy.Finally,the work and research results of this thesis are summarized,and the future work is summarized and prospect.