Journal of Terahertz Science and Electronic Information Technology , Volume. 20, Issue 12, 1298(2022)
Individual identification of Internet of things devices based on CLDNN
Abstract:Aiming at the problems of long signal sequence and poor feature robustness in Feature Engineering in individual recognition, the technology based on deep neural network is studied. Drawing lessons from Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks(CLDNN) in speech recognition, the local amplitude features of the signal are extracted through convolution neural network and the global time-domain features of the signal are extracted through long-term and shortterm memory network. A fully connected network is utilized to map the feature to the device label. Under the line of sight channel, the data of eight Lora modulated wireless data transmission stations are collected, and the Gaussian white noise is added to the simulation test. The simulation shows that when the Signal-to-Noise Ratio is low(0 dB) , the accuracy of the model can reach nearly 95% under the signal sequence length of 2 048 points. In addition, this model needs fewer parameters compared with VGG16 model. The proposed model has a certain application prospect in the deployment of Internet of things devices.
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WANG Fan, LU Dongming, WANG Hanhong. Individual identification of Internet of things devices based on CLDNN[J]. Journal of Terahertz Science and Electronic Information Technology , 2022, 20(12): 1298
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Received: Sep. 30, 2021
Accepted: --
Published Online: Feb. 17, 2023
The Author Email: Fan WANG (wangfan_01@126.com)