research-article

Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks

Authors:

Daqing ZhangAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2

Article No.: 312, Pages 1 - 29

https://doi.org/10.1145/3476053

Published: 18 October 2021 Publication History

Abstract

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.

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Cited By

Duan DSun ZNi TLi SJia XXu WLi TOkoshi TKo JLiKamWa R(2024)F2Key: Dynamically Converting Your Face into a Private Key Based on COTS Headphones for Reliable Voice InteractionProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661860(127-140)Online publication date: 3-Jun-2024
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Wang CFeng YZhong LZhu SZhang CZheng SLiang CWang YHe CYu CShi Y(2024)UbiPhysioProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435528:1(1-27)Online publication date: 6-Mar-2024
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Wang ZWang YYang J(2024)EarSlideProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435158:1(1-29)Online publication date: 6-Mar-2024
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Index Terms

Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Computer supported cooperative work
  2. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 5, Issue CSCW2

CSCW2

October 2021

5376 pages

EISSN:2573-0142

DOI:10.1145/3493286

Editor:
Jeff Nichols
Apple Inc., United States

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Copyright © 2021 ACM.

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Publication History

Published: 18 October 2021

Published in PACMHCI Volume 5, Issue CSCW2

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Cited By

Duan DSun ZNi TLi SJia XXu WLi TOkoshi TKo JLiKamWa R(2024)F2Key: Dynamically Converting Your Face into a Private Key Based on COTS Headphones for Reliable Voice InteractionProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661860(127-140)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661860
Wang CFeng YZhong LZhu SZhang CZheng SLiang CWang YHe CYu CShi Y(2024)UbiPhysioProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435528:1(1-27)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643552
Wang ZWang YYang J(2024)EarSlideProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435158:1(1-29)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643515
Ahmad NLeung H(2024)HyperHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435118:1(1-29)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643511
Xu ZLiu TJiang RHu PGuo ZLiu C(2024)AFaceProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435108:1(1-33)Online publication date: 6-Mar-2024
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Jiang FWang HLi Y(2024)VesNet: A Vessel Network for Jointly Learning Route Pattern and Future TrajectoryACM Transactions on Intelligent Systems and Technology10.1145/363937015:2(1-25)Online publication date: 28-Mar-2024
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Duan DChen YXu WLi T(2024)EarSEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314477:4(1-33)Online publication date: 12-Jan-2024
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Xia KLi WGan SLu S(2024)TS2ACTProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314457:4(1-22)Online publication date: 12-Jan-2024
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Chen WHu YSong WLiu YTorralba AMatusik W(2024)CAvatarProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314247:4(1-24)Online publication date: 12-Jan-2024
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Miao SChen LHu R(2024)Spatial-Temporal Masked Autoencoder for Multi-Device Wearable Human Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314157:4(1-25)Online publication date: 12-Jan-2024
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