Toward a real-time and budget-aware task package allocation in spatial crowdsourcing

Wu, Pengkun; Ngai, Eric W.T.; Wu, Yuanyuan

doi:10.1016/j.dss.2018.03.010

Cited by 44 publications

(30 citation statements)

References 55 publications

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“…In both phases, we measured the performance of our proposed Bayesian Network-based (BN) task matching against three other approaches. The approaches are based on three baseline algorithms which are Greedy algorithm [3], [10], [22], [23], kNN, time-weighted kNN algorithm [21] and Genetic Algorithm [12]. In the current experiment, both the synthetic and real-world datasets were used to simulate a realistic SC scenario so as to demonstrate the feasibility of the proposed approach.…”

Section: Methodsmentioning

confidence: 99%

“…The aim of the framework was to maximize the number of assigned tasks under budget constraints. [22] designed a real-time, and budget-aware task allocation mechanism to maximize the number of assigned tasks, and to improve the expected quality of the completed task under limited budget constraints. The mechanism also considers the distance of each worker from the tasks and the worker's performance in previously assigned tasks.…”

Section: Related Workmentioning

confidence: 99%

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A Framework for Optimal Worker Selection in Spatial Crowdsourcing Using Bayesian Network

Abdullah

Rahman

et al. 2020

IEEE Access

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Spatial Crowdsourcing (SC) is a new paradigm of crowdsourcing applications. Unlike traditional crowdsourcing, SC outsources tasks to distributed potential workers, and those who accept the task are required to travel to a predefined location to complete it. Currently, the primary aim of SC is to maximize the number of matched tasks or to minimize the travelling distances of the workers. However, less focus is given in matching the right tasks to the right workers, particularly in a heterogeneous tasks environment. To address this lacking, our work provides an efficient framework for selecting optimal workers for every task with various specification (geographical proximity, domain types, and expiration times), based on workers' attributes (task domain-specific knowledge, expertise or performance history, distance to task location, and task workload distribution). We introduce the use of Bayesian Network in modelling and selecting optimal workers, and use k-medoids partitioning technique for tasks clustering and scheduling. Our experimental results on both synthetic and real-world large datasets have shown that our proposed approach has outperformed other baseline approaches, in terms of low average error rate and fast execution time.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Framework for Optimal Worker Selection in Spatial Crowdsourcing Using Bayesian Network

Abdullah

Rahman

et al. 2020

IEEE Access

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“…20 In crowdsourcing contests, the participation history factors that include participation recency and frequency, winning recency and frequency, and tenure and last performance of the competitors are also derived in a study. 21 One of the authors suggests "personalized task recommendation" approach, which aims the matching of the worker interest with the appropriate task and thus makes the 10 Task and participant matching 0.5 0.5 0 0.5 1.5 Nassar and Karray 3 Overview of the crowdsourcing process 0.5 1 0 1 2.5 Javadi Khasraghi and Aghaie 21 Crowdsourcing contests 1 1 0.5 1 3.5 Zheng et al 58 Task design in crowdsourcing 0.5 0.5 0.5 0.5 2 Sales Fonteles et al 59 Trajectory recommendation of tasks 1 0.5 0.5 0.5 2.5 Burnap et al 30 Identifying experts in the crowd for evaluation of engineering designs 0.5 0.5 0.5 0.5 2 Ali-Hassan and Allam 60 Comparing crowdsourcing initiatives toward a typology development 0 0 0 1 1 Wang et al 61 Mobile crowdsourcing framework, challenges, and solutions 0 0 0 1 1 Ghezzi et al 62 Crowdsourcing review 0 0.5 0 1 1.5 Xintong et al 63 Brief survey of crowdsourcing for data mining 0 0 0 1 1 Pournajaf et al 38 Crowd sensing task assignment 0 0 0 1 1 Tarasov et al 64 Worker reliability in crowdsourcing 0 0 0 1 1 Baba et al 39 Improper task detection in crowdsourcing 0.5 0.5 0.5 1 2.5 Geiger and Schader 22 Ptask recommendation in crowdsourcing 0.5 0.5 0 1 2 Hosseini et al 16 Crowdsourcing: A taxonomy and systematic mapping study 0.5 0.5 0 0.5 1.5 Ye and Kankanhalli 65 Organizational task crowdsourcing 0.5 0 0 1 1.5 Ellero et al 66 Real-time crowdsourcing 0 0 0.5 1 1.5 Mao et al 1 Crowdsourcing survey in software engineering 0.5 0.5 0.5 1 2.5 Morschheuser et al 67 Gamified crowdsourcing conceptualization, literature review, and future agenda 0 0 0 1 1 Safran and Che 29 Real-time recommendation algorithms for crowdsourcing systems 1 1 1 1 4 Younas et al 68 Optimal task assignment 0.5 0 1 0.5 2 Harman and Azzam 69 Crowdsourcing criteria and standards 0.5 0 0 0.5 1 Moayedikia et al 31 Task assignment in crowdsourcing platforms 0.5 0 1 0 1.5 Wu et al 70 Task allocation in crowdsourcing 0.5 0.5 1 0 2 Sarı et al…”

Section: What Are the Task Assignment Framework/models Available For Effective Crowdsourcing?mentioning

confidence: 99%

Crowdsourcing usage, task assignment methods, and crowdsourcing platforms: A systematic literature review

ZhenYing

KhanAbdullah

Nazir

et al. 2021

J Software Evolu Process

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Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence.Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process, a total of 120 relevant studies have been identified, and then the most relevant 70 studies were selected to include as part of the current study. The proposed study shows the effect of task assignment in crowdsourcing, such as if a task is assigned to an appropriate worker or an inappropriate worker, what will be the consequences.The study also highlights crowdsourcing usage in the field of software engineering.All the existing task assignment methods used for assigning the task to make crowdsourcing activity more effective have been analyzed. The study also highlights all the available crowdsourcing platforms used for a variety of task to be performed.The study concludes by identifying the issues regarding the task assignment and to specify the methods for enhancement in the assignment of tasks for future research in crowdsourcing.

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“…They use the spatial attributes of the problem, namely spatial distribution and user travel costs, to propose the minimum location entropy priority and close distance priority strategies to address these challenges. Wu et al [25] proposed a real-time, budget-aware spatial crowdsourcing task allocation (RB-TPSC) model, with the goal of increasing task allocation rates and maximizing the expected outcome quality of the staff under a limited budget. The proposed RB-TPSC model can automatically make decisions on task allocation.…”

Section: Related Workmentioning

confidence: 99%

Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

Yang

Ban

Xing

2019

Sensors

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With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.

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Toward a real-time and budget-aware task package allocation in spatial crowdsourcing

Cited by 44 publications

References 55 publications

A Framework for Optimal Worker Selection in Spatial Crowdsourcing Using Bayesian Network

A Framework for Optimal Worker Selection in Spatial Crowdsourcing Using Bayesian Network

Crowdsourcing usage, task assignment methods, and crowdsourcing platforms: A systematic literature review

Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

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