Multi-Attribute Crowdsourcing Task Assignment With Stability and Satisfactory

Xing, Yuping; Wang, Liangmin; Li, Zhiyuan; Zhan, Yongzhao

doi:10.1109/access.2019.2941045

Cited by 12 publications

(7 citation statements)

References 36 publications

(37 reference statements)

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“…Yu et al [22] proposed a dynamic utility task allocation algorithm that estimates the ability of workers to execute tasks and then maximizes the overall utility. Xing et al [23] proposed an intelligent multi-attribute crowdsourcing task assignment approach. This approach solves the stability of the crowdsourcing transaction and adopts the game theory to maximize the satisfaction of both sides during the task assignment.…”

Section: Related Workmentioning

confidence: 99%

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni¹,

Ghazzai²,

Alelyani³

et al. 2021

Preprint

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Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: i) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and ii) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its Social Network (SN) neighbors. We first formulate the recruitment as an Integer Linear Program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: level of expertise, social relationship strength, recruitment cost, and recruiter's confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on Graph Neural Networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers' search space and afterwards, exploiting a meta-heuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world dataset illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

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Section: Related Workmentioning

confidence: 99%

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni¹,

Ghazzai²,

Alelyani³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Given that the fuzzy judgment matrix A-B is reciprocal one, the non-linear programming modification of the Fuzzy Preference Programming (FPP) method that only rely on the elements of the upper right part for matrix A-B is used to estimate weights (Formula ( 7)). In formula (7), can be notated as the value of the consistency index and each w i be expressed as weight of attribute.…”

Section: Determination Of Weights and Consistency Checkmentioning

confidence: 99%

“…To maximize human individual or collective intelligence in crowd computing, researchers have studied task assignment that allows one to assign real-time crowd tasks to appropriate user crowd for execution [7][8][9]. The majority of existing task assignment approaches in the crowd computing literature consider user behaviors as homogenous [10]; thereby, completely ignoring the influence of such behaviors on the recycling results.…”

mentioning

confidence: 99%

A crowdsourcing method for online social networks security assessment based on human-centric computing

Zhang

Jing

Wang

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Crowdsourcing and crowd computing are a trend that is likely to be increasingly popular, and there remain a number of research and operational challenges that need to be addressed. The human-centric computational abstraction called situation may be used to cope with these difficulties. In this paper, we focus on one such challenge, which is how to assign crowd assessment tasks about security and privacy in online social networks to the most appropriate users efficiently, effectively and accurately. Specifically, here we propose a novel task assignment method to facilitate crowd assessment, which improves the security and trustworthiness of social networking platforms, as well as a task assignment algorithm based on SocialSitu, which is a social-domain-focused situational analytics. Findings from our crowd assessment experiments on a real world social network Shareteches show that the precision and recall of the proposed method and algorithm are 0.491 and 0.538 higher than those of a random algorithm’s, as well as 0.336 and 0.366 higher than users’ theme-aware algorithm’s, respectively. Moreover, these results further suggest that our experimental evaluation enhance the security and privacy of online social networks.

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“…Yu et al [24] proposed a dynamic utility task allocation algorithm that estimates workers ability to achieve tasks and then maximizes the overall utility. Xing et al [25] studied an intelligent multi attributes crowdsourcing task assignment approach while considering stability and satisfactory. This approach solves the stability of the crowdsourcing transaction and adopts the game theory to maximize the satisfaction of both sides during the task assignment.…”

Section: Related Workmentioning

confidence: 99%

Many-to-Many Recruitment and Scheduling in Spatial Mobile Crowdsourcing

2020

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Spatial mobile crowdsourcing (SMCS) enables a task requester to commission workers to physically travel to specific locations to perform a set of spatial assignments (i.e., tasks are related to a specific geographical location besides time). To efficiently perform such tasks and guarantee the best possible quality of returned results, optimizing the worker recruitment and task assignment processes must be conducted. Because both workers and task requesters impose certain criteria, this procedure is not obvious. To tackle this issue, we propose a novel formulation of the SMCS recruitment where task matching and worker scheduling are jointly optimized. A Mixed Integer Linear Program (MILP) is first developed to optimally maximize the quality of matching measured as a weighted score function of different recruitment metrics while determining the trajectory of each selected worker executing tasks. To cope with NP-hardness, we propose a heuristic SMCS recruitment approach allowing the achievement of sub-optimal matching and recruitment solution by iteratively solving a weighted bipartite graph problem. Simulation results illustrate the performance of the SMCS framework for selected scenarios and show that our proposed SMCS recruitment algorithm outperforms an existing greedy recruitment approach. Moreover, compared to the optimal MILP solution, the proposed SMCS recruitment approach achieves close results with significant computational time saving.

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Multi-Attribute Crowdsourcing Task Assignment With Stability and Satisfactory

Cited by 12 publications

References 36 publications

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

A crowdsourcing method for online social networks security assessment based on human-centric computing

Many-to-Many Recruitment and Scheduling in Spatial Mobile Crowdsourcing

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