Research On New Model Of P-tensor Compressed Sensing And Secure Data Transmission Scheme

As an important application of Internet of things technology in the field of intelligent transportation,with the rapid development of the fifth generation mobile communication technology in China,the Internet of vehicles has gradually developed from the initial communication network between vehicles to more complex communication networks such as vehicle to person,vehicle to network,vehicle to infrastructure.The mutual communication between various sensors in the Internet of vehicles constitutes a wireless sensor network.On the one hand,the energy consumption of sensors is a problem that needs to be considered in practical use because the power reserve of sensors is not enough to support long-term high-energy operation.On the other hand,the portability of the sensor leads to its small size and limited storage capacity.Therefore,it is necessary to ensure the efficient use of storage space.In the Internet of vehicles,the richness of business scenarios is accompanied by the increase of image data types,which contains a large number of sensitive image data related to car owners.Therefore,the security of image data transmission is also a key issue to be considered.The characteristics of compressed sensing for image data compression and encryption at the same time can not only ensure security,but also realize data compression,which matches with the image data transmission requirements of wireless sensor network in the Internet of vehicles.However,the traditional compressed sensing is limited by the dimension matching requirement of matrix multiplication.For a large number of image data with different sizes,it is necessary to design the measurement matrix corresponding to the size and need a lot of storage space to store.Therefore,this paper proposes a new model of p-tensor compressed sensing and applies the proposed model to the image data transmission of wireless sensor networks in the Internet of vehicles.Because p-tensor compressed sensing can use small measurement matrix to measure different sizes of data,which can effectively reduce the cost of storage and transmission.The main achievements and innovations of this paper are as follows(1)We proposed a new model of p-tensor compressed sensing.The proposed model realizes the flexible measurement of signal and reduces the amount of data to be stored.The model consists of two parts:matrix generation strategy and signal measurement.Through the simulation experiment,the signal recovery quality is analyzed,the influence of different matrices on the signal recovery quality is studied,and the influence of signal sparsity and sampling quantity on the recovery success rate is analyzed.In addition,the influence of parameters and signal size on the recovery error is analyzed,and the flexibility of the proposed model in measurement and the advantage in data storage are discussed.(2)We proposed a secure data transmission scheme based on the new model of p-tensor compressed sensing is proposed.The proposed scheme mainly combines chaotic scrambling,new p-tensor compressed sensing model and optical image encryption technology.Firstly,the encrypted data is processed by chaotic scrambling to improve the security of data transmission.P-tensor product improves the flexibility of matrix operation and reduces the storage space.Optical image encryption technology can realize double image encryption at the same time and improve the efficiency of data encryption.All kinds of matrices in the proposed scheme are generated by chaotic system,which reduces the amount of data transmitted.Through theoretical analysis and simulation experiments,the energy-saving effect,flexibility,image restoration performance,encryption effect and security of the proposed scheme are studied.In addition,it is compared with the similar schemes in the same industry at home and abroad.The results show that the proposed scheme can achieve more efficient and secure data encryption transmission.