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Information transmission mode and IoT community reconstruction based on user influence in opportunistic s ocial networks

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Abstract

With the wide popularization of the 5G network and the technology of IoT, a variety of mobile terminals has become a necessity for people’s daily life. In the popular environment of the mobile terminals, the high-frequency radio wave mainly promoted by 5G communication has obvious deficiencies in network coverage, which makes the opportunistic network particularly important at present. At present, there are many routing algorithms for opportunistic networks, but most of them only consider the distance between nodes, the occupation of resources, and the success probability of transmission. It leads to the underutilization of a large amount of valuable information brought by each node itself and makes the message transmission and users’ usage habits separate, which causes lots of meaningless message transmission and is prone to information leakage. According to this, this paper establishes an information transmission mode in opportunistic social networks based on user influence (ITMUI), which uses the analysis of the influence of nodes in the network community to filter and purposefully transmit information by taking the influence factor as a part of the path weight, so as to further improve the transmission efficiency of the opportunistic network. Moreover, after considering the information of the node itself, the communication of the network can change with the change of the environment and the node. The research of this strategy is mainly divided into two parts. The first part is the analysis of user influence, and the second part is the reconstruction of the IoT community structure combined with the analysis results. Simulation results show that ITMUI has 6% less transmission delay than other models and 12% higher delivery ratio than the average model, which means that compared with other algorithms, this algorithm costs less and can significantly improve the data transmission efficiency.

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Acknowledgements

Availability of data and materials: all data analyzed during the current study are included in the submission.

Funding

This work was supported in the Hunan Provincial Natural Science Foundation of China (2018JJ3299, 2018JJ3682).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Chang sha, 410083, China

    Jia Wu, Jiahao Xia & Fangfang Gou

  2. Research Center for Artificial Intelligence, Monash University, Clayton, VIC 3800, Australia

    Jia Wu

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  1. Jia Wu
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Correspondence to Fangfang Gou.

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Wu, J., Xia, J. & Gou, F. Information transmission mode and IoT community reconstruction based on user influence in opportunistic s ocial networks. Peer-to-Peer Netw. Appl. 15, 1398–1416 (2022). https://doi.org/10.1007/s12083-022-01309-4

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