Research On False Data Detection Algorithm Of Environmental Monitoring Wireless Sensor Network

As Wireless Sensor Networks（WSN）play a more and more important role in kinds of fields,security issues have become a crucial issue for WSN.False data intrusion is a typical attack method.It will not only interfere with the normal communication of the network,cause the network to collect wrong information and affect the user’s decision-making,but also greatly consume the limited node resources and shorten the network life cycle.Aiming at the intrusion and attack behavior of false data,based on the Compressed Sensing（CS）theory,the original signal can be quickly reconstructed by collecting a small amount of information data in real time,and the spatial interpolation algorithm which is widely used in spatial information,a new false data detection algorithm based on the fusion of the Compressed Sensing and spatial interpolation is proposed.This article mainly conducts in-depth research on compressed sensing theory and spatial interpolation algorithm data reconstruction.The innovative basic work is two points:（1）Aiming at the hidden danger of false data intrusion in environmental monitoring,a false data detection algorithm based on the fusion of compressed sensing and spatial interpolation is proposed.The detection algorithm is mainly based on the different response characteristics of the two algorithms to false data.For the collected monitoring area data,we use spatial interpolation and compressed sensing to obtain reconstruction results,and study the relevant features between the two for intrusion detection of false data.This detection algorithm achieves no waste of sensor node resources,and it is insensitive to the form of false data intrusion to make more widely applicable.（2）Aiming at the poor image reconstruction quality of CS algorithms and solving large-scale sparse problems,an improved compressed sensing reconstruction algorithm based on7)₁-regularized least squares（7）₁＿7))is proposed.7)₁＿7)uses the pre-processed conjugate gradient algorithm to calculate the search direction,which greatly reduces the amount of calculation,and can solve the sparse problem of multiple variables and observations in a short time,and provides a new method for solving the problem of convex optimization.The MATLAB platform is used to design the simulation experiment.Two-dimensional image reconstruction experiments show that compared with the classic reconstruction algorithm,the original image can be reconstructed better,and it shows a better image reconstruction effect.We apply the improved algorithm to the detection algorithm of false data in environmental monitoring,and the results further illustrate that the proposed false data detection algorithm is effective and feasible for false data detection schemes.