Research On Data Compression And Encryption Technology For Healthcare And Smart Home

At present,less attention has been paid to human health status,home appliance health status and home energy-saving management in smart homes.Based on the network data sharing analysis provided by healthcare and smart home systems with the above characteristics,it can help human health warning,energy-saving management and extend home appliances Service life.A large amount of data will be generated by real-time health detection of home appliances and human bodies,and these data,especially health data,have certain privacy.The direct transmission of smart home IoT data through the Internet will lead to data leakage and threaten personal property and life safety.In order to solve the above problems,this thesis fully analyzes the data sources in smart homes,and focuses on the data compression and encryption technologies for health smart home.In-depth research on power data compression and data transmission security based on physiological parameter keys in the complete working mode of home appliances is carried out.The main contents are as follows:(1)Electric power data compression method under the complete working mode of home appliances.Fully studied the characteristics of transient and steady-state signals of current data of different types of home appliances,and proposed an adaptive waveform data compression method based on similarity segmentation and resampling.The auto-correlation coefficients are automatically segmented to obtain steady-state and transient data.After resampling the steady-state data,Fast Fourier Transformation(FFT)and ZLIB cascade compression are used.For transient data,ZLIB lossless compression is used.And put forward the evaluation index of comprehensive compression system.Tested with microwave ovens,air conditioners,and washing machines.The experimental results show that: under the condition of guaranteeing the degree of distortion,the sampling rate with a smaller comprehensive compression coefficient is preferred;among the three resampling methods under 5 sampling rates,the sampling is resampled to 128 points per cycle The overall effect is better,the compression ratio is higher than 21.73,and the root mean square error percentage is within 1.04%.(2)Encryption key sequence generation method.The time and frequency domain characteristics of pulse signals are studied,and a feature extraction and key sequence generation method based on physiological parameters is proposed.Wavelet denoising is performed on the waveform data of the original pulse signal to remove the noise and baseline drift during the pulse signal acquisition process,and then the time domain characteristics of the pulse signal are extracted using the differential method and the interval extremum method,and the pulse waveform is divided according to the time domain feature points For the weekly wave,calculate the FFT of the weekly wave to extract the frequency domain features of the pulse signal,then splice the time domain features with the frequency domain features in order to obtain the key sequence,and finally verify the randomness of the key sequence through the NIST SP800-22 statistical test tool.The experiment compares the two methods of generating the key sequence of the pulse signal.The experimental results prove that the key sequence in this thesis has good randomness.(3)Data transmission encryption method.Introduced the commonly used symmetric encryption Advanced Confidential Standard(AES),asymmetric encryption elliptic curve encryption algorithm(ECC)and hash algorithm(SHA3),using the characteristics of the three encryption algorithms,combined The above research results of data compression and key sequence generation of physiological parameters give an encryption method for the safe transmission of data for healthcare and smart homes.The key sequence of AES and ECC is generated through physiological parameters.Combining with the data compression method,three data encryption methods are designed in this chapter.The experimental results show that the data encryption method proposed in this thesis has good security and adaptability.The research work in this thesis lays a certain theoretical foundation for the efficient and safe transmission of data in the health smart home system.The algorithm in this thesis can be further optimized and directly transplanted to the embedded system for application.Figure[43] Table[12] Reference[75]...