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Abstract
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Recently, 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 d
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