Design And Research On CS-based Wireless Sensor Network Spatial Sparse Signals Network Models

With the conception of the Internet of the thing (IOT) coming up, China makes itthe direction of guiding the whole information industry chain development, to realizethe boom of enconomy. As one of the key technologies, Wireless Sensor Networks(WSN) has been more widespread concerned on solving the information sensingproblems by minimum cost and maximum flexibility. Therefore, large-scalehigh-density network expansion, huge amounts of data transfer and storage, energylimited and other key technology bottleneckes to address immediate. Compressivesensing theory (CS) has innovative ideas on data measuring transmission andreconstruction, and provides a new technical means to solve the wireless sensornetwork to explore new signal acquisition technology and signal processing.Based on CS, we explored research on wireless sensor networks informationcollection, transmission and processing technologies to study the whole networkperformance:(1) estimated CS-based wireless sensor network model, do research onthe contribution of the compressed sensing theory for wireless sensor networks,makes wireless sensor networks more universal;(2) analyze the sparsity of the naturalsignals, built a more adaptable sparse decomposition platform helps wirlessmonitoring more accurate in the reconstruction of the target signals;(3) research andsort out the reconstruction algorithm for compressed sensing theory, establish a new,more suitale for wireless sensor networks unknown sparsity of the signalreconstruction method;(4) analyze the CS-based wireless sensor networksperformation in network global energy consumption, load, transmission and otheraspects advantages, proposed easier implement sensing and measureing mechanism.The exhaustive research work and achievement are as follows:(1) Study the sparsity of the signals in the natural time domain, frequencydomain non-sparse signal sparse domain to explore, considering the limited hardware,make use of Gabor atoms and Chirplet atoms in generating complete dictionary andthe corresponding twice optimization algorithm, making any sparse signal can find a sparse basal sparse representation in the sparse dictionary. The study results show thatcompared to the sparse decomposition method, the Gabor sparse dictionary andChirplet sparse dictionary are more universal sparseness to achieve lower sufficiently,higher sparse precision, the vast majority of signals are able to get a better sparseeffect. In addition, the Gabor dictionary twice optimal matching algorithm and theChirplet dictionary twice optimal matching algorithm can make the sparsedecomposition faster and occupy less storage space, but also more conducive to theenhancement of the reconstruction accuracy.(2) Analyzed the existing reconstruction algorithms, compare reconstructionaccuracy, speed, and iterative method, the iteration termination condition, thecomputational complexity and other aspects of comprehensive optimization andimprovement, and propose a more suitable for large-scale wireless sensor networks,data reconstruction algorithm, backtracking adaptive threshold iterative matchingchasing algorithm (BATIMP), based on the RIP constraints, realized faster, higheraccuracy, greater stability, high probability reconstruction of the original signal. Theresults show that the CS-based wireless sensor networks, more suitable for the natureof a variety of sparse unknown but compressible signal, adaptively select the step size,to save time and improve the reconstruction speed with traditional reconstructioncompared to a certain extent reduces the hardware requirements on the reconstructionside, making a reconstruction algorithm that compressed sensing theory to computerhardware are able to achieve.(3) Estimated the distributed compression of sensor network model, proposedthe space sparse signal network model, research on the CS-based wireless sensornetwork data acquisition and measurement mechanisms. Depending on topology androuting for wireless sensor networks, involved compressed sensing into the wirelesssensor networks measuring projection, signal acquisition and transmission, design aGaussian loop/Bernoulli loop semi-random measurement matrix, making theCS-based wireless sensor networks project number of information in sensing process,during multi-hop transmission, it will be able to not only reduce the dimensions ofglobal network information, the great improvement in the level of bandwidthutilization, but also make network load more balanced and transmission of multi-hop wireless sensor network is able to realize. The results show that compared withtraditional wireless sensor networks, the CS-based wireless sensor networks in termsof local and global energy consumption and load balancing can have a high degree ofoptimization, and has the freedom in the choice of network topology. Thesemi-random cycle measurement sensor in the CS-based wireless sensor networks, itcan keep the random measurement matrix and most of the sparse matrix-relatedadvantages, but also make more simple the design and deployment of the networknodes, only corresponding coefficient of the measurement according to the networkdistribution of seeds and node ID than the traditional random matrix generated ismore suitable for wireless sensor networks with limited storage space, at the sametime, the network initialization, more suitable for practical applications of thenetwork.In short, involving of compressive sensing theory,we introduce the new conceptof measuring the transmission for wireless sensor networks, the exact reconstructionof the data also provides a higher quality of reconstruction effect, which is moresuitable for a variety of the natural signals, has the "adaptive".