发表状态 | 已发表Published |
题名 | Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks |
作者 | |
发表日期 | 2021-10-18 |
发表期刊 | Proceedings of the ACM on Human-Computer Interaction
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ISSN/eISSN | 2573-0142 |
卷号 | 5期号:CSCW2 |
摘要 | With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach. |
关键词 | cooperative co-evolution geo-social networks mobile crowdsourcing task propagation model |
DOI | 10.1145/3476053 |
URL | 查看来源 |
语种 | 英语English |
Scopus入藏号 | 2-s2.0-85117952118 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/7029 |
专题 | 个人在本单位外知识产出 |
作者单位 | 1.Northwestern Polytechnical University, Xi'an, China 2.University of Macau, Macao, China 3.Beijing Normal University, Beijing, China 4.Jilin University, Changchun, China 5.Peking University, Beijing, China |
推荐引用方式 GB/T 7714 | Wang, Liang,Yu, Zhiwen,Yang, DIngqiet al. Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks[J]. Proceedings of the ACM on Human-Computer Interaction, 2021, 5(CSCW2). |
APA | Wang, Liang., Yu, Zhiwen., Yang, DIngqi., Wang, Tian., Wang, En., .. & Zhang, Daqing. (2021). Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2). |
MLA | Wang, Liang,et al."Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks". Proceedings of the ACM on Human-Computer Interaction 5.CSCW2(2021). |
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