Incentive Mechanism for Task Diffusion in Social Network-Based Mobile Crowdsourcing
Fanlei Kong, Bing Shi, Yan Lu · IEEE Transactions on Computational Social Systems · 2025
With the increasing prominence of smart mobile devices, mobile crowdsourcing (MCS) has become an innovative distributed computing paradigm. However, it faces the challenge of users being less willing to participate. Social networks can help diffuse tasks, thereby recruiting more potential users to participate in crowdsourcing tasks. Therefore, we exploit social networks to recruit users by adopting the independent cascade model for diffusing crowdsourcing tasks to increase the number of participants. Considering the time-sensitive crowdsourcing tasks and the delays in the diffusion process, we model the interaction between registered users and the platform as a reverse auction model. Furthermore, the participating users may strategically provide untruthful information to make more profits. Therefore, we design a reverse auction-based incentive mechanism to incentivize users to diffuse tasks while preventing strategic behavior about reporting diffusion costs. We prove that the proposed mechanism can satisfy the desirable properties of incentive compatibility, individual rationality, and computational efficiency. We evaluate the proposed mechanism against four benchmark strategies on real and synthetic datasets. The experimental results show that our mechanism can recruit more mobile users and achieve higher task completion.