Time and cost-effective online advertising in social Internet of Things using influence maximization problem

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dc.authoridBouyer, Asgarali/0000-0002-4808-2856
dc.authorwosidBouyer, Asgarali/JOZ-6483-2023
dc.authorwosidBouyer, Asgarali/IYS-5116-2023
dc.contributor.authorMolaei, Reza
dc.contributor.authorFard, Kheirollah Rahsepar
dc.contributor.authorBouyer, Asgarali
dc.date.accessioned2024-05-19T14:46:06Z
dc.date.available2024-05-19T14:46:06Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractRecently, a novel concept called the Social Internet of Things (SIoT) has emerged, which combines the Internet of Things (IoT) and social networks. SIoT plays a significant role in various aspects of modern human life, including smart transportation, online healthcare systems, and viral marketing. One critical challenge in SIoT-based advertising is identifying the most effective objects for maximizing advertising impact. This research paper introduces a highly efficient heuristic algorithm named Influence Maximization-Cost Minimization for Advertising in the Social Internet of Things (IMCMoT), inspired by real-world advertising strategies. The IMCMoT algorithm comprises three essential steps: Initial preprocessing, candidate objects selection and final seed set identification. In the initial preprocessing phase, the objects that are not suitable for advertising purposes are eliminated. Reducing the problem space not only minimizes computational overhead but also reduces execution time. Inspired by real-world advertising, we then select influential candidate objects based on their effective sociality rate, which accounts for both the object's sociality rate and relevant selection cost factors. By integrating these factors simultaneously, our algorithm enables organizations to reach a broader audience at a lower cost. Finally, in identifying the final seed set, our algorithm considers the overlapping of neighbors between candidate objects and their neighbors. This approach helps minimize the costs associated with spreading duplicate advertisements. Through experimental evaluations conducted on both real-world and synthetic networks, our algorithm demonstrates superior performance compared to other state-of-the-art algorithms. Specifically, it outperforms existing methods concerning attention to influence spread, achieves a reduction in advertising cost by more than 2-3 times and reduces duplicate advertising. Additionally, the running time of the IMCMoT algorithm is deemed acceptable, further highlighting its practicality and efficiency.en_US
dc.identifier.doi10.1007/s11276-023-03496-1
dc.identifier.endpage710en_US
dc.identifier.issn1022-0038
dc.identifier.issn1572-8196
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85173974596en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage695en_US
dc.identifier.urihttps://doi.org10.1007/s11276-023-03496-1
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5442
dc.identifier.volume30en_US
dc.identifier.wosWOS:001077730400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofWireless Networksen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectOnline Advertisingen_US
dc.subjectSocial Networksen_US
dc.subjectSocial Internet Of Thingsen_US
dc.subjectInfluence Maximizationen_US
dc.subjectCost Minimizationen_US
dc.titleTime and cost-effective online advertising in social Internet of Things using influence maximization problemen_US
dc.typeArticleen_US

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