Budget-Feasible Diffusion Mechanisms for Mobile Crowdsourcing in Social Networks
Mobile crowdsourcing has emerged as a popular approach for organizations to leverage the collective intelligence of a crowd of users to obtain services. Considering users’ costs for providing services, it is vital for the requester to design incentive mechanisms to encourage users’ participation in crowdsourcing under the budget constraint. This aligns with the concept of budget-feasible mechanism design. Existing budget-feasible mechanisms often assume immediate user reachability and willingness of joining the crowdsourcing, which is unrealistic. To address this issue, a promising approach is to have participating users diffuse auction information to potential users in the social network. However, this brings another challenge in that participating users can be strategic and therefore hesitant to invite more potential competitors to join the crowdsourcing platform. In this paper, we focus on developing diffusion mechanisms that incentivize strategic users to actively diffuse auction information through the social network. This helps to attract more informed users and ultimately increases the value of the procured services. Specifically, we propose optimal budget-feasible diffusion mechanisms that simultaneously guarantee individual rationality, budget-feasibility, strong budget-balance, incentive-compatibility (i.e., users report real costs and diffuse auction information to all their neighbors) and approximation. Experiment results under real datasets further demonstrate the efficiency of proposed mechanisms.
Authors
- Junzhou Luo (ORCID: https://orcid.org/0000-0001-7518-4367)
- Weiwei Wu (ORCID: https://orcid.org/0000-0001-9172-6955)
- Wanyuan Wang (ORCID: https://orcid.org/0000-0002-9080-4971)
- Minming Li (ORCID: https://orcid.org/0000-0002-7370-6237)
- Yingchao Zhao (ORCID: https://orcid.org/0000-0001-8362-6735)
- Xiang Liu (ORCID: https://orcid.org/0000-0001-6672-5870)
- Yifan Qin (ORCID: https://orcid.org/0009-0000-0568-9116)
Institutions
- City University of Hong Kong (HK)
- Southeast University (CN)
Publication Details
- Journal
- IEEE Transactions on Mobile Computing
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1109/tmc.2025.3549751
- Citations
- 2
- Primary Topic
- Mobile Crowdsensing and Crowdsourcing
- Type
- article
- Field-Weighted Citation Impact
- 3.39