Efficient Energy Saving Scheme For Secure Data Transmission And Legal Node Authentication In Wireless Sensor Networks

With the development of big data era,as one of the research hotspots in the field of information,wireless sensor networks(WSNs)are composed of many spatially dispersed sensor sensing nodes that cooperate with each other,and have the function of collecting,processing and transmitting data for signals in special environments.The application of WSNs can automatically monitor and respond to forest fires,avalanches,hurricanes and other emergencies,and the deployment of WSNs in national public facilities,transportation,hospitals and wider areas can effectively monitor and collect information.As WSNs are used more and more widely,the research of WSNs faces more and more challenges.There are mainly two challenges to be solved:The first challenge is how to lessen the energy expenditure to maximize the lifetime of the node.The second challenge is how to ensure the confidentiality,authentication and unforgeability of data in an open environment where security cannot be guaranteed,and how to detect nodes damaged by attackers.Therefore,it is very meaningful to study a technology that can ensure data confidentiality and save energy in WSNs.For the sake of disposing of the above problems,this paper applies the deep learning method,trains the network based on the depth compression sensing theory,and conducts relevant research around the data sampling and reconstruction method based on depth compression sensing and the efficient and secure data transmission in WSNs.The main research innovations of this paper are presented as follows:(1)A data acquisition and reconstruction technology based on deep learning and compressed sensing is proposed.The compression sensing sampling network(CSSN)and joint reconstruction network(JRN)are trained in advance before deploying the network to improve the sampling efficiency and data reconstruction accuracy.The node of WSNs is aware of the data side,and the CSSN is used for efficient and energy-saving data sampling.At the sink node with sufficient energy,high precision data recovery is achieved by applying JRN.The simulation experiment shows that for the big data sampling,the application of CSSN is at least 5 times faster than the traditional compression sensing sampling,which is a great improvement in WSNs,and the recovered data has a high reconstruction accuracy.(2)An efficient and secure data transmission scheme based on deep compression sensing is proposed.Thanks to the use of depth compression sensing,the proposed scheme can achieve efficient data acquisition and reconstruction with low sampling rate and high-precision reconstruction;At the same time,since the nodes in WSNs compress the collected data,energy consumption and memory space occupation are saved during data transmission.In order to enhance the security of data transmission in WSNs and authenticate the identity of nodes,our scheme uses lightweight Hash algorithm to realize node identity identification and authentication at the sink node side;At the same time,in cryptography,the Paillier addition homomorphic encryption algorithm is applied to encipher the data transmitted in the network to realize the confidential transmission of data.On this basis,the proposed project is compared with other similitude projects,and simulation experiments and in-depth analysis are carried out in terms of runtime and security performance to check the efficiency and security of the proposed scheme.