Research Of A New Compressed Sensing Model And Its Application In The Internet Of Things

With the rapid development of sensor technology,communication network and the Internet of Things technology,many application scenarios,such as intelligent transportation,health care,environmental monitoring,smart home and so on,have gradually emerged to facilitate people's lives.However,due to the limited resources of sensor nodes and the use of wireless communication,it is essential to ensure that the key and sensitive information data in these monitoring applications can be transmitted and processed in a secure,efficient and real-time way.Compressed sensing can realize sampling,compression and encryption at the same time,and it has been widely used in the acquisition and processing of the information in wireless sensor networks.The data generated in the Internet of Things is usually diverse and massive,but traditional compressed sensing needs to store a large number of measurement matrices to match the signals with different dimensions,which consumes a lot of resources of sensor nodes,such as storage,calculation and energy.Therefore,it is of great practical significance to explore a more efficient and flexible compressed sensing model.Aiming at the above two key issues,this thesis carries out in-depth research and analysis around a new compressed sensing model and a secure and efficient data transmission framework,which are based on compressed sensing theory and chaos theory.The main innovations and research results are described below.(1)A new double protection compressed sensing model with high flexibility and its parallel reconstruction algorithm are proposed.The two low-dimensional sub-matrices realize the measurement of the signal through tensor product operation,which not only breaks the limitation of the dimension matching condition and reduces resource consumption,but also improves the reconstruction efficiency.From both theoretical and experimental aspects,the unique and accurate reconstruction conditions,feasibility,high efficiency,robustness,flexibility and lightweight security of the proposed new model are verified and analyzed.(2)Based on the excellent encryption properties of the chaotic system,such as non-periodicity,unpredictability,ergodicity,pseudo-randomness and initial value sensitivity.In this thesis,according to the data transmission requirements in monitoring applications of the Internet of Things,a secure and efficient data transmission system,which further combines quantization and a round of bidirectional diffusion operations,is designed based on the proposed new compressed sensing model.The control parameters,initial values and sampling distances of the two chaotic systems are taken as the secret keys to transmit and store.Both theoretical analysis and simulation experiments show that the proposed system has time-efficiency,resource-efficiency(low computational complexity,less storage space,less energy consumption),strong security and excellent image encryption performance.