Real-Time Cross Online Matching in Spatial Crowdsourcing

Cheng, Yurong; Li, Boyang; Zhou, Xiangmin; Yuan, Ye; Wang, Guoren; Chen, Lei

doi:10.1109/icde48307.2020.00008

Cited by 38 publications

(3 citation statements)

References 17 publications

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“…Song et al [24] present trichromatic online matching in real-time spatial crowdsourcing which contains three entities of workers, tasks, and workplaces. Cheng et al [25] propose a cross-online matching that enables the platform to borrow some unoccupied workers from other platforms. Zhao et al [26] first consider reducing the average waiting time of users, and many complete tasks so as to improve the user experience.…”

Section: Single-worker Planningmentioning

confidence: 99%

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing

Zhou¹,

Wan²,

Zhang³

et al. 2023

Preprint

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With the prevalence of mobile devices and ubiquitous wireless networks, spatial crowdsourcing has attracted much attention from both academic and industry communities. On spatial crowdsourcing platforms, task requesters can publish spatial tasks and workers need to move to destinations to perform them. In this paper, we formally define the Skilled Task Assignment with Extra Budget (STAEB), which aims to maximize total platform revenue and achieve fairness for workers and task requesters. In the STAEB problem, the complex task needs more than one worker to satisfy its skill requirement and has the extra budget to subsidize extra travel cost of workers to attract more workers. We prove that the STAEB problem is NP-complete. Therefore, two approximation algorithms are proposed to solve it, including a greedy approach and a game-theoretic approach. Extensive experiments on both real and synthetic datasets demonstrate the efficiency and effectiveness of our proposed approaches.

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Section: Single-worker Planningmentioning

confidence: 99%

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing

Zhou¹,

Wan²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Song et al [29] present trichromatic online matching in real-time spatial crowdsourcing which contains three entities of workers, tasks, and workplaces. Cheng et al [30] propose a cross-online matching that enables the platform to borrow some unoccupied workers from other platforms. Zhao et al [31] first consider reducing the average waiting time of users, and many complete tasks so as to improve the user experience.…”

Section: Single-worker Planningmentioning

confidence: 99%

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing

Zhou¹,

Wan²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

With the prevalence of mobile devices and ubiquitous wireless networks, spatial crowdsourcing has attracted much attention from both academic and industry communities. On spatial crowdsourcing platforms, task requesters can publish spatial tasks and workers need to move to destinations to perform them. In this paper, we formally define a new problem, i.e., the Skilled Task Assignment with Extra Budget (STAEB), which aims to maximize total platform revenue and achieve fairness for workers and task requesters. In the STAEB problem, the complex task needs more than one worker to satisfy its skill requirement and has the extra budget to subsidize extra travel cost of workers to attract more workers. We prove that the STAEB problem is NP-complete. Therefore, two approximation algorithms are proposed to solve it, including a greedy approach and a game-theoretic approach. Extensive experiments on both real and synthetic datasets demonstrate the efficiency and effectiveness of our proposed approaches.

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“…Thus, the major challenge of the spatial crowdsourcing platforms is how to assign the large-scale tasks to their workers, i.e., task assignment. In addition, most of the existing studies focus on task assignment based on the whole study area [8], [9], [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Clustering Based Priority Queue Algorithm for Spatial Task Assignment in Crowdsourcing

Ma,

Gao,

Bhatti

et al. 2024

IEEE Trans. Serv. Comput.

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Spatial crowdsourcing is an increasingly popular category in the era of mobile Internet and sharing economy, where tasks have spatio-temporal constraints and must be completed at specific locations. In this paper, we focus on the Multi-Objective Spatio-Temporal task assignment (MOST) problem considering the worker heterogeneity in spatial crowdsourcing and model it as a combinatorial multi-objective optimization (MOO) problem with the goals of maximizing the overall task completion rate and minimizing the average task time cost. Finding the optimal global assignment turns out to be intractable since it does not simply imply optimality for an individual worker, as a typical nearest-neighbor heuristic generally does not render a satisfactory result. We prove that the problem is NP-hard. Subsequently, we formulate an efficient algorithm for the MOST problem -Task Clustering based Mixed Priority Queue Scheduling (TAMP). First, we improve the spectral clustering algorithm to evenly divide the task network into different subdomains according to tasks' geographical locations, considering the task clustering phenomena in real scenarios. We then design a mixed priority queue strategy considering the geographical influence and temporal urgency, to schedule workers finishing tasks in sequence. Experiments on synthetic and real datasets demonstrate the efficiency of our solution over other methods.

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Real-Time Cross Online Matching in Spatial Crowdsourcing

Cited by 38 publications

References 17 publications

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing

Clustering Based Priority Queue Algorithm for Spatial Task Assignment in Crowdsourcing

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