Research On Data Collection Strategy Of Large-Scale Wireless Sensor Networks Based On Compressed Sensing

In general,massive sensing data generated by wireless sensor networks records and describes a certain state of the monitored object.However,due to the characteristics of the sensor network itself,the sensing data collected by the node may have a large amount of redundancy due to the temporal and spatial correlation.Therefore,we need to design an effective data collection strategy to effectively eliminate redundant data and extract valuable information from the data.Compressed sensing theory is a novel strategy in many sensing data compression and collection strategies.Compressed sensing theory has three important contents,which are the sparse representation of signals under specific transform bases,construction of observation matrices unrelated to specific bases,and reconstruction signals algorithm.Compressed sensing theory states that only a compressible signal,or a sparse signal under a specific transform bases,can be used to reconstruct the original signal from a small number of samples using a reconstruction algorithm by solving an optimization problem.The above approach is intended to reduce the amount of data redundancy in the network.Thesis focuses on the above problems,using compressed sensing as the theoretical basis,studies the sparseness of signals and the routing strategy based on compressed sensing(namely,the design of observation matrix in compressed sensing).The main contributions are as follows:(1)Collecting sensing data: including real signals and artificially synthesized signals.First,100 ZigBee nodes were deployed in the real world to form a ZigBee sensor network,and sampled signals from eight different scenarios were collected.Secondly,in order to study the compressive sensing performance of a specific signal,we designed artificially synthetic signals.(2)Designing orthogonal transform bases: In compressed sensing,different orthogonal transforms make the signal sparsity different.In order to better study the sparsity of the signal,we have designed two orthogonal transforms,namely Row-trans transform and Col-trans transform.Through 1,800 experiments,it is found that,compared with Fourier transform,discrete cosine transform and wavelet transform,the Row-trans transform and the Col-trans transform have better sparsity,the reconstruction error is relatively stable and the error value is smaller,which is significantly better than other orthogonal transforms,and the reconstructed signal is closer to the original signal.(3)Constructing Observation Matrix: Designing a Novel Routing Strategy.Based on the Leach-c clustering routing protocol and combined Bernoulli random distribution,we propose a CS_leach-c algorithm applied to the theory of compressed sensing.Through experiments,it is found that the CS_leach-c strategy not only prolongs the network lifetime,but also effectively reduces the energy consumption in the network.Obviously,the CS_leach-c strategy satisfies the requirements of the wireless sensor network for routing protocols.