Visible to the public Node Identification in Wireless Network Based on Convolutional Neural Network

TitleNode Identification in Wireless Network Based on Convolutional Neural Network
Publication TypeConference Paper
Year of Publication2018
AuthorsShen, Weiguo, Wang, Wei
Conference Name2018 14th International Conference on Computational Intelligence and Security (CIS)
Date Publishednov
KeywordsAd hoc networks, Ad-Hoc Network, Collaboration, computational complexity, convolution, convolutional neural nets, convolutional neural network, convolutional neural networks, Deep Learning, feature extraction, gradient methods, learning (artificial intelligence), local feature extraction, Metrics, Neural Network Security, node identification, policy-based governance, principal component analysis, pubcrawl, resilience, Resiliency, softmax model, stochastic gradient descent method, Training, wireless network, wireless networks, Wireless sensor networks
AbstractAiming at the problem of node identification in wireless networks, a method of node identification based on deep learning is proposed, which starts with the tiny features of nodes in radiofrequency layer. Firstly, in order to cut down the computational complexity, Principal Component Analysis is used to reduce the dimension of node sample data. Secondly, a convolution neural network containing two hidden layers is designed to extract local features of the preprocessed data. Stochastic gradient descent method is used to optimize the parameters, and the Softmax Model is used to determine the output label. Finally, the effectiveness of the method is verified by experiments on practical wireless ad-hoc network.
Citation Keyshen_node_2018