Multi-Worker-Aware Task Planning in Real-Time Spatial Crowdsourcing

Qian, Tao; Zeng, Yuxiang; Zhou, Zimu; Tong, Yongxin; Lei, Chen; Xu, Ke

doi:10.1007/978-3-319-91458-9_18

Cited by 30 publications

(8 citation statements)

References 22 publications

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“…multiple workers collaborate on a task). The planning modes are to maximize general utility (e.g., satisfaction scores [27,28], payoffs [18,[29][30][31], distance [32][33][34]).…”

Section: Multi-workers 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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“…multiple workers collaborate on a task). The planning modes are to maximize general utility (e.g., satisfaction scores [27,28], payoffs [18,[29][30][31], distance [32][33][34]).…”

Section: Multi-workers 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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“…Zhao et al 19 proposed a novel preference‐aware task assignment system based on workers' temporal preferences, including history‐based context‐aware tensor decomposition for workers' temporal preferences modeling and preference‐aware task assignment. Tao et al 20 proposed a new problem called the multiworker‐aware task planning (MWATP) problem in the online scenario. We not only assign tasks to workers but also make plans for them so that the total utility is maximized.…”

Section: Related Workmentioning

confidence: 99%

Reward optimization of spatial crowdsourcing for coalition‐based maintenance task

Hao

Zheng

Yang

et al. 2022

Int J of Intelligent Sys

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Spatial crowdsourcing (SC) can use the moving workers to achieve location‐based tasks. It has been widely used in takeout, data labeling, organizing activities, security and artificial intelligence. Currently, many manufacturers wish to explore SC in maintenance business, as SC can reduce maintenance time, reduce labor costs, and improve customer satisfaction. In maintenance business scenarios, there are two kinds of cooperated participators. One is freedom workers who are sensitive to distance, and the other is employed by manufacturers who are insensitive to distance. Therefore, some methods of SC with one type worker are challenging to apply to maintenance business scenarios directly. To match this scenario, we model the maintenance scenario and prove that this scenario is an NP‐hard problem; then, both greedy and Nash equilibrium methods are proposed to complete the tasks for making a high total reward. The greedy algorithm (GA) first assigns the nearest available workers to each task, and the employee will try to join the task to help GM to complete more tasks, on the condition that the task cannot be completed and reaches a particular proportion. The Nash equilibrium algorithm (NA) is used to find a Nash equilibrium for all the workers and employees. The experiments demonstrate the efficiency and effectiveness of the synthetic data set of gMission in small and large data sets. The finished task number of NA is about 5% more than that of GA, and the reward of NA is about 10% more than that of GA.

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“…[18] considers the problem of flexible online matching where workers can be scheduled if no task is assigned. [11] recommends routes dynamically for workers to deal with online tasks, and the goal is to maximize the total utility. [21] assigns tasks to workers while trading off quality and latency of task completion.…”

Section: Task Assignment In Spatial Crowdsourcingmentioning

confidence: 99%

Specialty-Aware Task Assignment in Spatial Crowdsourcing

Song

Zhu

2018

Artificial Intelligence and Symbolic Computation

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With the rapid development of Mobile Internet, spatial crowdsourcing is gaining more and more attention from both academia and industry. In spatial crowdsourcing, spatial tasks are sent to workers based on their locations. A wide kind of tasks in spatial crowdsourcing are specialty-aware, which are complex and need to be completed by workers with different skills collaboratively. Existing studies on specialty-aware spatial crowdsourcing assume that each worker has a united charge when performing different tasks, no matter how many skills of her/him are used to complete the task, which is not fair and practical. In this paper, we study the problem of specialty-aware task assignment in spatial crowdsourcing, where each worker has fine-grained charge for each of their skills, and the goal is to maximize the total number of completed tasks based on tasks' budget and requirements on particular skills. The problem is proven to be NP-hard. Thus, we propose two efficient heuristics to solve the problem. Experiments on both synthetic and real datasets demonstrate the effectiveness and efficiency of our solutions.

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Multi-Worker-Aware Task Planning in Real-Time Spatial Crowdsourcing

Cited by 30 publications

References 22 publications

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

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

Reward optimization of spatial crowdsourcing for coalition‐based maintenance task

Specialty-Aware Task Assignment in Spatial Crowdsourcing

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