{"title":"Detection of Effective Devices in Information Dissemination on the Complex Social Internet of Things Networks Based on Device Centrality Measures","authors":"Wei Deng, Junqi Deng, Peyman Arebi","doi":"10.1155/cplx/2919169","DOIUrl":null,"url":null,"abstract":"<div>\n <p>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.</p>\n </div>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2025 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/2919169","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complexity","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/cplx/2919169","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.