期刊
IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 20, 期 5, 页码 2055-2066出版社
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2020.2973958
关键词
Task analysis; Social network services; Recruitment; Sensors; Crowdsourcing; Mobile computing; Real-time systems; Mobile crowdsourcing; worker recruitment; incentive; social network
资金
- National Natural Science of China [61872274, 61702562, 61822207, U1636219, U19A2067, 61772377, 61672257, 91746206]
- Equipment Pre-Research Joint Fund of Ministry of Education of China (Youth Talent) [6141A02033327]
- National Key R&D Program of China [2019YFA0706403]
- Natural Science Foundation of Hubei Province [2017CFB503, 2017CFA047]
- Young Tlite Scientists Sponsorship Program by CAST [2018QNRC001]
- Young Talents Plan of Hunan Province of China [2019RS2001]
- Science and Technology planning project of ShenZhen [JCYJ20170818112550194]
- Fundamental Research Funds for the Central Universities [2042018gf0043, 2042019gf0098]
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (Wuhan University of Science and Technology)
This paper focuses on addressing the problem of insufficient worker participation in mobile crowdsourcing systems with limited number of workers, proposing to leverage social networks for worker recruitment, task completion, and worker pool expansion. By introducing a dynamic incentive mechanism and a task-specific epidemic model, the proposed approach effectively motivates workers to participate in task propagation and completion, dynamically updating rewards to maximize task completion within financial constraints. Extensive experimental results demonstrate that the SocialRecruiter outperforms existing approaches in terms of worker recruitment and task completion.
Worker recruitment is an important problem in mobile crowdsourcing (MCS), which aims to find sufficient and suitable participants to perform tasks. However, existing worker recruitment approaches mainly focus on how to select the most suitable workers for tasks from a large worker pool, while the recruitment problem under insufficient workers (e.g., a new MCS system) has not been well addressed. In this paper, we focus on the insufficient participation problem of MCS systems with limited number of workers, and propose to leverage social network to recruit workers for task completion as well as expanding the worker pool. To this end, we propose a dynamic incentive mechanism, called SocialRecruiter, to encourage workers on the MCS platform to propagate tasks through social networks, so that inviting friends to join in the MCS platform to further propagate and complete tasks. Motivated by the SIR epidemic model, we propose a novel task-specific epidemic model to characterize the status change of users for task propagation and completion through social networks. In order to encourage task completion and propagation, the propagating reward and completing reward are provided according to workers' actions. In particular, in order to maximize the task completion within the financial budget, the propagating and completing rewards are dynamically updated at each cycle according to real-time worker recruitment progress. The extensive experimental results on two real-world datasets demonstrate that SocialRecruiter outperforms the state-of-the-art approaches in terms of worker recruitment and task completion.
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