Message Transmission Strategy Based on Recurrent Neural Network and Attention Mechanism in Iot System

• Published in: Journal of circuits, systems, and computers Vol. 31; no. 7
• Main Authors: Gou, Fangfang, Wu, Jia
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 15.05.2022; World Scientific Publishing Co. Pte., Ltd
• Subjects: Customization; Data transmission; Demand; Electronic devices; Internet of Things; Neural networks; Recurrent neural networks; User behavior; personalized ranking; delivery ratio; historical behavior; Internet of Things; attention mechanism; recursive network
• DOI: 10.1142/S0218126622501262
• ISSN: 0218-1266

With the popularization of the Internet of Things technology and the improvement of 5G communication technology, the influence of mobile devices on the network structure is increasing. The devices in the network are usually regarded as social users that transmit information. Because the movement of users is dynamic and random, it is more difficult for complex networks to grasp the changing rules of their topological structure. The data transmission model established by considering only the historical behavior of users can no longer meet the demand for fast transmission of large-capacity data. Based on this, this paper proposes a dynamic personalized data transmission model (GRDPS) that considers the recurrent neural network and attention mechanism. First, it uses a recurrent neural network to build users’ personalized preferences and model the user’s historical behavior. Then, GRDPS introduces an attention mechanism to dynamically weight historical user behaviors based on the user’s current message transmission. It is different from the previous methods of modeling user historical behaviors. Based on the requirements of user dynamics, GRDPS effectively considers the temporal characteristics of user historical behaviors and automatically learns the evolution law of user behaviors. Based on the demand of user randomness, GRDPS fully considers the characteristic correlation between the user’s historical behavior and current transmission demand. Finally, GRDPS combines these two points to obtain a personalized ranking of users. The simulation results show that the delivery rate of GRDPS is up to 0.95. Moreover, its data transmission delay and network overhead are better than other methods in the experiment.