High Quality Participant Recruitment of Mobile Crowdsourcing over Big Data

Li, S; Zhang, J; Xie, D; Yu, S; Dou, W

High Quality Participant Recruitment of Mobile Crowdsourcing over Big Data

Li, S Zhang, J Xie, D Yu, S

Dou, W

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Publication Type:: Conference Proceeding
Citation:: 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, 2018
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Li, S	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Xie, D	en_US
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743	en_US
dc.contributor.author	Dou, W	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, 2018	en_US
dc.identifier.isbn	9781538647271	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134619
dc.description.abstract	© 2018 IEEE. With the rich set of embedded sensors installed in smart-phones, an increasing number of applications have been designed based on these mobile sensors rather than on static sensors in urban areas. In Mobile Crowdsourcing (MCS), participant selection is promoted to save energy and entire incentives. Nevertheless, most of the current researches on this problem assume that the system should get the entire information about the participants. As a result, the suitable tasks are always not allocated to the suitable participants. This practice contributes an inaccurate match between a task and participants, which leads to energy and incentives waste. In view of this challenge, we aim to select participants under a more accurate prediction model, rather than assuming that the information of each participant should be obtained in advance. The prediction model is enabled by the big data of participants' historic evaluation, which are used to predict the user action. Furthermore, a greedy method based on an improved singular value decomposition (SVD), named as SVD-G, is proposed to solve this problem. Finally, the proposed SVD-G method is validated by using the large-scale dataset collected from a real-world project (DaZhongDianPing APP).	en_US
dc.relation.ispartof	2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings	en_US
dc.relation.isbasedon	10.1109/GLOCOM.2018.8647720	en_US
dc.title	High Quality Participant Recruitment of Mobile Crowdsourcing over Big Data	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. With the rich set of embedded sensors installed in smart-phones, an increasing number of applications have been designed based on these mobile sensors rather than on static sensors in urban areas. In Mobile Crowdsourcing (MCS), participant selection is promoted to save energy and entire incentives. Nevertheless, most of the current researches on this problem assume that the system should get the entire information about the participants. As a result, the suitable tasks are always not allocated to the suitable participants. This practice contributes an inaccurate match between a task and participants, which leads to energy and incentives waste. In view of this challenge, we aim to select participants under a more accurate prediction model, rather than assuming that the information of each participant should be obtained in advance. The prediction model is enabled by the big data of participants' historic evaluation, which are used to predict the user action. Furthermore, a greedy method based on an improved singular value decomposition (SVD), named as SVD-G, is proposed to solve this problem. Finally, the proposed SVD-G method is validated by using the large-scale dataset collected from a real-world project (DaZhongDianPing APP).

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http://hdl.handle.net/10453/134619