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Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing. / Wang, Jiangtao; Wang, Feng; Wang, Yasha et al.
In: IEEE Transactions on Mobile Computing, Vol. 18, No. 7, 01.07.2019, p. 1661-1673.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Wang, J, Wang, F, Wang, Y, Zhang, D, Wang, L & Qiu, Z 2019, 'Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing', IEEE Transactions on Mobile Computing, vol. 18, no. 7, pp. 1661-1673. https://doi.org/10.1109/TMC.2018.2865355

APA

Wang, J., Wang, F., Wang, Y., Zhang, D., Wang, L., & Qiu, Z. (2019). Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing. IEEE Transactions on Mobile Computing, 18(7), 1661-1673. https://doi.org/10.1109/TMC.2018.2865355

Vancouver

Wang J, Wang F, Wang Y, Zhang D, Wang L, Qiu Z. Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing. IEEE Transactions on Mobile Computing. 2019 Jul 1;18(7):1661-1673. Epub 2018 Aug 11. doi: 10.1109/TMC.2018.2865355

Author

Wang, Jiangtao ; Wang, Feng ; Wang, Yasha et al. / Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing. In: IEEE Transactions on Mobile Computing. 2019 ; Vol. 18, No. 7. pp. 1661-1673.

Bibtex

@article{00a84fd4e0004abab431250c3e5f3d20,
title = "Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing",
abstract = "Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.",
keywords = "Crowdsourcing, Mobile computing, mobile crowd sensing, Optimization, Recruitment, Sensors, smart city, social network, Social network services, Task analysis, Worker recruitment",
author = "Jiangtao Wang and Feng Wang and Yasha Wang and Daqing Zhang and Leye Wang and Zhaopeng Qiu",
note = "{\textcopyright}2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2019",
month = jul,
day = "1",
doi = "10.1109/TMC.2018.2865355",
language = "English",
volume = "18",
pages = "1661--1673",
journal = "IEEE Transactions on Mobile Computing",
issn = "1536-1233",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "7",

}

RIS

TY - JOUR

T1 - Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

AU - Wang, Jiangtao

AU - Wang, Feng

AU - Wang, Yasha

AU - Zhang, Daqing

AU - Wang, Leye

AU - Qiu, Zhaopeng

N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.

AB - Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.

KW - Crowdsourcing

KW - Mobile computing

KW - mobile crowd sensing

KW - Optimization

KW - Recruitment

KW - Sensors

KW - smart city

KW - social network

KW - Social network services

KW - Task analysis

KW - Worker recruitment

U2 - 10.1109/TMC.2018.2865355

DO - 10.1109/TMC.2018.2865355

M3 - Journal article

AN - SCOPUS:85051648436

VL - 18

SP - 1661

EP - 1673

JO - IEEE Transactions on Mobile Computing

JF - IEEE Transactions on Mobile Computing

SN - 1536-1233

IS - 7

ER -