Research on network cognition model and mechanism of intelligent information network

• Published in: China communications Vol. 20; no. 2; pp. 257 - 277
• Main Authors: Dong, Nannan, Yin, Hao, Ren, Baoquan, Li, Hongjun, Gong, Xiangwu, Zhong, Xudong, Han, Junmei, Lyu, Jiazheng
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01.02.2023; Institute of Systems Engineering,AMS,PLA,Beijing 100000,China%Institute of Systems Engineering,AMS,PLA,Beijing 100000,China; College of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150001,China
• Subjects: Artificial intelligence; Decision making; Electromagnetics; intelligent information network; Internet; Knowledge engineering; multi-domain perception; network cognition; network knowledge; Optimization; Wireless networks; network knowl-edge; network cognition; intelligent information network; multi-domain perception
• ISSN: 1673-5447
• DOI: 10.23919/JCC.2023.02.017

Intellectualization has been an inevitable trend in the information network, allowing the network to achieve the capabilities of self-learning, self-optimization, and self-evolution in the dynamic environment. Due to the strong adaptability to the environment, the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network (IIN), making the traditional definition of the intelligent information network no longer appropriate. Moreover, the thinking capability of existing IINs is always limited. This paper redefines the intelligent information network and illustrates the required properties of the architecture, core theory, and critical technologies by analyzing the existing intelligent information network. Besides, we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network. The proposed model can perceive the overall environment data of the network and extract the knowledge from the data. As the model's core, the knowledge guides the model to generate the optimal decisions adapting to the environmental changes. At last, we present the critical technologies needed to accomplish the proposed network cognition model.