Task Scheduling in Three-Dimensional Spatial Crowdsourcing: A Social Welfare Perspective

Wang, L; Yang, D; Yu, Z; Xiong, F; Han, L; Pan, S; Guo, B

Task Scheduling in Three-Dimensional Spatial Crowdsourcing: A Social Welfare Perspective

Wang, L Yang, D Yu, Z Xiong, F Han, L Pan, S Guo, B

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Mobile Computing, 2023, 22, (9), pp. 5555-5567
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Wang, L
dc.contributor.author	Yang, D
dc.contributor.author	Yu, Z
dc.contributor.author	Xiong, F
dc.contributor.author	Han, L
dc.contributor.author	Pan, S
dc.contributor.author	Guo, B
dc.date.accessioned	2024-05-13T01:30:58Z
dc.date.available	2024-05-13T01:30:58Z
dc.date.issued	2023-09-01
dc.identifier.citation	IEEE Transactions on Mobile Computing, 2023, 22, (9), pp. 5555-5567
dc.identifier.issn	1536-1233
dc.identifier.issn	1558-0660
dc.identifier.uri	http://hdl.handle.net/10453/178854
dc.description.abstract	Until recently, a novel spatial crowdsourcing paradigm, namely Three-Dimensional (3D) spatial crowdsourcing, has emerged, in which the task requestors and the workers need travel to their designated third-party workplaces, e.g., shared offices, to deliver certain services, such as DiDi station ride-sharing service, Quyundong sport training service in the Online-To-Offline (O2O) applications. In 3D spatial crowdsourcing applications, a core issue is to develop an efficient global tasklist plan, based on the tripartite matching among the three parties, i.e., task requestors, workers and workplaces, which is different from the conventional spatial crowdsourcing. In this context, one key challenge is how to suitably schedule the available workers with the consideration of the interests of all the parties, under the constraint of worker resource. To answer the questions, in this paper, we propose and study a new problem, namely Social-Welfare-driven Task Scheduling (SWTS) problem, which strives to schedule the workers' continuous routines, i.e., successively implementing tasks for different requestors at different workplaces, to promote the social welfare for all the involved parties. We prove our studied problem is NP-hard, and devise two heuristic optimization algorithms to solve it. Finally, we conduct extensive experiments which verify the efficiency and effectiveness of the proposed algorithms on both real and synthetic data sets.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Mobile Computing
dc.relation.isbasedon	10.1109/TMC.2022.3175305
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4604 Cybersecurity and privacy
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Task Scheduling in Three-Dimensional Spatial Crowdsourcing: A Social Welfare Perspective
dc.type	Journal Article
utslib.citation.volume	22
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-13T01:30:56Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	9

Abstract:

Until recently, a novel spatial crowdsourcing paradigm, namely Three-Dimensional (3D) spatial crowdsourcing, has emerged, in which the task requestors and the workers need travel to their designated third-party workplaces, e.g., shared offices, to deliver certain services, such as DiDi station ride-sharing service, Quyundong sport training service in the Online-To-Offline (O2O) applications. In 3D spatial crowdsourcing applications, a core issue is to develop an efficient global tasklist plan, based on the tripartite matching among the three parties, i.e., task requestors, workers and workplaces, which is different from the conventional spatial crowdsourcing. In this context, one key challenge is how to suitably schedule the available workers with the consideration of the interests of all the parties, under the constraint of worker resource. To answer the questions, in this paper, we propose and study a new problem, namely Social-Welfare-driven Task Scheduling (SWTS) problem, which strives to schedule the workers' continuous routines, i.e., successively implementing tasks for different requestors at different workplaces, to promote the social welfare for all the involved parties. We prove our studied problem is NP-hard, and devise two heuristic optimization algorithms to solve it. Finally, we conduct extensive experiments which verify the efficiency and effectiveness of the proposed algorithms on both real and synthetic data sets.

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http://hdl.handle.net/10453/178854