Coalition-based Task Assignment in Spatial Crowdsourcing

Zhao, Yan; Guo, Jiannan; Chen, Xuanhao; Hao, Jianye; Zhou, Xiaofang; Zheng, Kai

doi:10.1109/icde51399.2021.00028

Cited by 45 publications

(8 citation statements)

References 27 publications

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“…2(a), the running time of algorithms increases with the increment of the number of tasks. Similar with existing works [1,10,22,43], EBGT runs longer than RAN and GRY because it needs multiple iterations to find the optimal solution of each round continuously. However, the running time is totally acceptable even when the number of tasks reaches 1200, i.e., about 16 seconds.…”

Section: Experiments Setupmentioning

confidence: 96%

“…Recently, gaming-theoretic algorithms became popular. For example, Zhao et al [10,11] designed gaming-theoretic algorithms to form worker coalitions in order to maximize the overall rewards [10] and ensure the fairness of worker payoff [11], respectively.…”

Section: Multi-workers Planningmentioning

confidence: 99%

“…Previous works [8][9][10][11][12] on spatial crowdsourcing have focused on assigning complex tasks to multiple workers such that these workers can cover the skill needs of tasks while ignoring the additional travel cost of the workers. However, in reality, if workers want to complete the assigned task, they also need to move to the location of the task, which will incur travel costs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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 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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Section: Experiments Setupmentioning

confidence: 96%

Section: Multi-workers Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

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“…A game-theoretic model was developed to elucidate how Nash equilibrium efforts influence the net output of crowdsourcing networks and provide a formula for designing reward sharing schemes to maximize net production output [28]. In maximizing the total rewards of multiple workers in spatial task allocation, [29] proposed a coalitionbased task assignment (CTA) and designed greedy and equilibrium methods, utilizing a simulated annealing algorithm to find a superior Nash equilibrium. Lastly, Haggiag et al [30] proposed a game-theoretic model for crowdsourcing competitions, proving that in many instances, the organizer's utility maximization strategy is to favor the low-capacity winner in case of a tie.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Crowdsourcing Task Assignment Policies Using Multi-Agent Reinforcement Learning in Stochastic Games

Zhang,

He,

Chen

et al. 2023

Preprint

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Spatio-temporal crowdsourcing as a current widely used model, how to solve the equilibrium between the task planning efficiency and the worker's revenue is a challenging task, in order to overcome these problems, we firstly define crowdsourcing task allocation as a multi-party stochastic game, and solve the equilibrium for the case with perfect recall, which gives the optimal policy for the platform and the workers. Based on the previous work, we propose a framework based on the dual actor-critic approach, which learns the collaborative optimal strategies based on actor-critic. We solve the equilibrium by introducing a Monte Carlo tree search algorithm and use a strategy gradient-based approach to optimize the actor and critic parameters. To evaluate our approach, experiments are conducted in several different crowdsourcing scenarios. The experimental results show that our method is able to achieve good performance in different scenarios and has high accuracy and stability in equilibrium solving. We will open our source code to allow other researchers to reproduce our results.

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“…The heterogeneous nature of sensing devices is considered in [13] where the work utilizes particle swarm optimization algorithm (PSO) for the selection of users in order to increase the quantity of completed tasks. In contrast to this method, authors of the study [14] proposed a 3-stage task allocation method where the same data property are shared between different tasks. This method considers the spatial and temporal properties along with data property of sensory data.…”

Section: Related Workmentioning

confidence: 99%

A Multi Task Allocation Based Time Optimization Framework Using Social Networks in Mobile Crowd Sensing

Veerapathiran¹,

Ramachandran²

2022

I2M

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The quality of data and its sensing cost is the important concern for task allocation in crowd sensing. The sensing capabilities of a device to send the collection of sensor data to a cloud requires crowd sensing in order to receive reliable data. Crowd sensing is used in many areas such as traffic monitoring, smart cities, health care, transportations, environmental monitoring and many more. Most of existing works are only based on assumptions in task scheduling about the number of candidate users and are mainly performed optimization of single task allocation. If the candidate users are few, then the completion of task with in the schedule can be difficult for many sensor applications. In this work, we proposed a social network-based task allocation scheme for the optimization of multi task allocation. The main idea of the proposed work is to maximize the task completion within the allocated schedule. It is evident that the task scheduling algorithms are NP-hard and we introduced a decreasing threshold task allocation (DTT) and fast greedy selections (FGS) algorithms along with Crow COOT Foraging Optimization (CCFO) algorithm to allocate the tasks parallelly with maximum efficiency. The proposed algorithms such as C-DTT (CCFO-DTT) and C-FGS (CCFO-FGS_ are used for the efficient allocation of tasks. The combination of these algorithms can be helpful in selecting the candidate users who will perform the completion of maximum tasks. Due to the selection of proper users in each round, the time consumption of the tasks to be completed is greatly reduced. The experimental results also indicates that the proposed work performs well in the optimization of multi task allocation than the other state of the art models.

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Coalition-based Task Assignment in Spatial Crowdsourcing

Cited by 45 publications

References 27 publications

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

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

Optimizing Crowdsourcing Task Assignment Policies Using Multi-Agent Reinforcement Learning in Stochastic Games

A Multi Task Allocation Based Time Optimization Framework Using Social Networks in Mobile Crowd Sensing

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