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Detection of Effective Devices in Information Dissemination on the Complex Social Internet of Things Networks Based on Device Centrality Measures

Author

Listed:
  • Wei Deng
  • Junqi Deng
  • Peyman Arebi

Abstract

The Complex Social Internet of Things (CSIoT) integrates the connectivity of IoT with the relational dynamics of complex social networks, creating systems where devices autonomously form and manage relationships. The centrality measures specify the topological characteristics of each node in terms of local and global information of the node in the network. The detection of effective devices in disseminating information across CSIoT networks is critical for optimizing communication, improving network performance, and ensuring efficient resource utilization. In this paper, temporal centrality measures are used to identify influential devices in information dissemination. For this purpose, first, the centrality measures for SIoT network devices have been redefined, and then, using the SIR model, each of the measures has been evaluated in terms of the success rate in identifying effective devices in information dissemination. The results have shown that in SIoT networks that have a high clustering coefficient, the centrality measures of closeness and betweenness have a better performance in identifying influential devices that are effective in spreading information. Also, for networks that have a high degree of heterogeneity, the device coreness centrality and device Katz centrality measures perform better than other measures. Finally, the results show that mobile devices play a more important role in disseminating information than static devices.

Suggested Citation

  • Wei Deng & Junqi Deng & Peyman Arebi, 2025. "Detection of Effective Devices in Information Dissemination on the Complex Social Internet of Things Networks Based on Device Centrality Measures," Complexity, John Wiley & Sons, vol. 2025(1).
  • Handle: RePEc:wly:complx:v:2025:y:2025:i:1:n:2919169
    DOI: 10.1155/cplx/2919169
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    References listed on IDEAS

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    Cited by:

    1. Yan Dou & WanLin Liu & Peyman Arebi, 2025. "Robust Controllability Network Method on Temporal Network Using Temporal Link Prediction and Network Embedding," Complexity, John Wiley & Sons, vol. 2025(1).
    2. Jie Zhang & Ling Ding & Peyman Arebi, 2025. "ACO‐Based Neural Network to Enhance the Efficiency of Network Controllability of Temporal Networks," Complexity, John Wiley & Sons, vol. 2025(1).

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