Self-powered Compressed Sensing Sampling Model Based On Discontinuous Random Micro Environment Energy

Energy is very important for wireless sensor networks(WSNs).It is a major bottleneck of WSNs' lifetime and working time.Also,it is one of the hotspots of research all the time.WSNs usually have a large number of deployments,ultra-small size,special application environment and other characteristics,so manual battery replacement for WSN nodes is time-consuming and labor-intensive,which increases labor costs,sometimes is even extremely difficult and very unrealistic.Therefore,energy-harvesting wireless sensor networks(EHWSNs)emerge as the times require,providing a novel solution to WSNs energy constraints.The emergence of EHWSNs not only solves the problem of energy limitation in some application scenarios,but also faces new difficulties.Because the deployment of EHWSN node environment may lead to some problems such as bad use environment,mismatch between EHWSNs energy collection source and environment energy,the energy collected by EHWSNs has the characteristics of discontinuity,randomness,instability and unpredictability.Especially for the self powered EHWSNs in micro energy environment,it is necessary to improve the key technologies and management schemes of EHWSNs related to energy,such as sensor signal processing technology,data transmission protocol,working mode,etc.However,the existing energy management method of EHWSN node hardware structure still uses the traditional signal sampling technology to modify or optimize the node circuit locally,the circuit structure is still complex,the effect of energy consumption reduction is not obvious,and there is no optimization design of self powered nodes from the sampling theory.Therefore,from the point of view of the safety,power consumption and energy utilization efficiency of the self powered EHWSN node signal data,using the compressed sensing sampling theory,this paper focuses on the self powered compressed sensing sampling model,algorithm,circuit design and simulation analysis based on the discontinuous random micro environment,and puts forward the optimal design parameters,and finally realizes the safety of the EHWSNs node signal data Integrity and the goal of reducing the energy consumption of signal processing.The main contents of this paper are as follows:(1)In view of the security and energy consumption of the traditional EHWSN node signal data,according to the theory of compressed sensing sampling,the analog to information converter(AIC)of the hardware structure of compressed sensing sampling is designed.AIC carries on the simulation verification to the original signal compression and the restoration algorithm,the result shows that after the original signal passes through the AIC,the sampling output signal will become completely different from the original signal,reduced the special hardware and software encryption,not only guaranteed the self-powered node communication security,but also reduced the original signal sampling data quantity and the processing energy consumption significantly;(2)Aiming at the low efficiency and high energy consumption of traditional WSNs signal sampling,the overall framework of self-powered EHWSN node and self-powered compressed sensing sampling model circuit based on discontinuous random micro environment are designed innovatively,and the circuit analysis and qualitative simulation are carried out for the self-powered compressed sensing sampling model.It is found that the actual sampling rate of ADC through the self-powered compressed sensing sampling model is much smaller than the minimum sampling rate required by Nyquist sampling theory,which verifies the correctness of the model circuit design;(3)According to different input signal processing parameters,the boundary conditions and processing effect of the model are analyzed quantitatively,and the optimal parameters of sampling signal processing are put forward innovatively.The complexity simulation of self-powered compressed sensing sampling model is carried out.The influence of different input signal processing parameters on the sampling model is compared and simulated.The simulation results show that when the complexity of lemple Ziv signal is within 0.7 and its recovery effect is good after self-powered compression sensing sampling model.Then the performance of the self-powered compressed sensing sampling model is compared when the signal processing parameters are different.The comparison results show that the best compression rate of self-powered compressed sensing sampling model should be set in the range of 15% to 20%,that is,the amount of sampled data will be reduced by 50% to 62.5% compared with the minimum sampling rate required by Nyquist sampling theory;the frequency of PN signal generated by micro control unit should be about 5 times of the highest frequency of original input signal;The bandwidth or the highest frequency of the input signal is the same as the 3 dB cut-off frequency of the edge RC low-pass filter of the compressed sensing sampling module.Finally,the EHWSN node and AIC chip of Nyquist sampling theory are compared.The compressed sensing sampling can significantly reduce the task mode power of MCU by 41.87%.Moreover,the power consumption of the self-powered compressed sensing sampling model designed in this paper is far lower than that of the integrated AIC chip,and its function is more perfect.