Exploring the Internet of Things sequence-structure detection and supertask network generation of temporal-spatial-based graph convolutional neural network

Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

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Security Model of Internet of Things Based on Binary Wavelet and Sparse Neural Network

International Journal of Mobile Computing and Multimedia Communications ◽

10.4018/ijmcmc.2019010101 ◽

2019 ◽

Vol 10 (1) ◽

pp. 1-17

Author(s):

Zhihui Wang ◽

Jingjing Yang ◽

Benzhen Guo ◽

Xiao Zhang

Keyword(s):

Neural Network ◽

Internet Of Things ◽

Network Architecture ◽

Safety Management ◽

The Internet ◽

Security Model ◽

The Novel ◽

Simulated Environment ◽

Proposed Model ◽

The Internet Of Things

At present, the internet of things has no standard system architecture. According to the requirements of universal sensing, reliable transmission, intelligent processing and the realization of human, human and the material, real-time communication between objects and things, the internet needs the open, hierarchical, extensible network architecture as the framework. The sensation equipment safe examination platform supports the platform through the open style scene examination to measure the equipment and provides the movement simulated environment, including each kind of movement and network environment and safety management center, turning on application gateway supports. It examines the knowledge library. Under this inspiration, this article proposes the novel security model based on the sparse neural network and wavelet analysis. The experiment indicates that the proposed model performs better compared with the other state-of-the-art algorithms.

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The Internet of Things as a Deep Neural Network

IEEE Communications Magazine ◽

10.1109/mcom.001.2000015 ◽

2020 ◽

Vol 58 (9) ◽

pp. 20-25

Author(s):

Rong Du ◽

Sindri Magnusson ◽

Carlo Fischione

Keyword(s):

Neural Network ◽

Internet Of Things ◽

Deep Neural Network ◽

The Internet ◽

The Internet Of Things

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A laguerre neural network-based ADP learning scheme with its application to tracking control in the Internet of Things

Personal and Ubiquitous Computing ◽

10.1007/s00779-016-0916-x ◽

2016 ◽

Vol 20 (3) ◽

pp. 361-372 ◽

Cited By ~ 25

Author(s):

Xiong Luo ◽

Yixuan Lv ◽

Mi Zhou ◽

Weiping Wang ◽

Wenbing Zhao

Keyword(s):

Neural Network ◽

Internet Of Things ◽

Tracking Control ◽

The Internet ◽

Learning Scheme ◽

The Internet Of Things

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Key Technologies of Collaborative Design and Manufacturing of Mechanical and Electrical Products Based on Internet of Things

Mobile Information Systems ◽

10.1155/2021/8936762 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Kun Shang

Keyword(s):

Neural Network ◽

Internet Of Things ◽

Collaborative Design ◽

Rbf Neural Network ◽

Electromechanical System ◽

The Internet ◽

Neural Network Controller ◽

Network Controller ◽

Design And Manufacturing ◽

The Internet Of Things

An electric motor driven by the electromechanical system of the Internet of Things is attractive because of its long life capability of the propulsion system. In this paper, the application of collaborative design and manufacturing in the design automation of IOT electromechanical system is reviewed, and the application of collaborative design and manufacturing in robots, a typical IOT electromechanical system, is described in detail. In this paper, we explain five aspects including the construction of a multiangle unified modeling method for the electromechanical system of the Internet of Things; the constraint processing mechanism for the optimization problem of the electromechanical system of the Internet of Things; the constraint multiobjective optimization methods; design methods that integrate constraint multipurpose evolutionary algorithms and knowledge extraction; and design automation of visual perception systems for electromechanical systems based on the Internet of Things and deep neural networks. The research shows that under the control of a conventional radial basis function neural network controller and the control of a radial basis function neural network controller based on the electromechanical system of the Internet of Things, the system will be affected to a certain extent when there is interference. Under the control of a traditional RBF neural network controller, the system requires 0.18 seconds to restore stability. When using the RBF neural network controller based on the electromechanical system of the Internet of Things, the system returns to a stable state after 0.09 s, and the peak time is reduced by 59% compared with the conventional RBF neural network controller.

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