Quality-aware Online Task Assignment in Mobile Crowdsourcing

Miao, Xin; Kang, Yanrong; Ma, Qiang; Liu, Kebin; Chen, Lei

doi:10.1145/3397180

Cited by 16 publications

(10 citation statements)

References 42 publications

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“…Miao et al [25,26] assigned crowdsourcing task allocation dependent on participant position. The aim is to use a quality-aware online task assignment (QAOTA) algorithm to improve overall efficiency for location-based tasks.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

Sleem¹,

Mekky²,

El–Sappagh³

et al. 2022

Computers, Materials &Amp; Continua

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The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques, such as the internet of things (IoT) and mobile crowdsensing (MCS). The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively, with each mobile user completing much simpler micro-tasks. This paper discusses the task assignment problem in mobile crowdsensing, which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals. The goal is to minimize aggregate sensing time for mobile users, which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality. This paper introduces a two-phase task assignment framework called location time-based algorithm (LTBA). LTBA is a framework that enhances task assignment in MCS, whereas assigning tasks requires overlapping time intervals between tasks and mobile users' tasks and the location of tasks and mobile users' paths. The process of assigning the nearest task to the mobile user's current path depends on the ant colony optimization algorithm (ACO) and Euclidean distance. LTBA combines two algorithms: (1) greedy online allocation algorithm and (2) bio-inspired traveldistance-balance-based algorithm (B-DBA). The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user. B-DBA was location-based and worked on maximizing total task quality. The results demonstrate that the average task quality is 0.8158, 0.7093, and 0.7733 for LTBA, B-DBA, and greedy, respectively. The sensing time was reduced to 644, 1782, and 685 time units for LTBA, B-DBA, and greedy, respectively. Combining the algorithms improves task assignment in MCS for both total task quality and sensing time. The results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time, and the greedy algorithm follows it then B-DBA.

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Section: Related Workmentioning

confidence: 99%

“…(Continued) [25] and [26] Quality maximization. Polynomial-time (online) and approximation algorithm (offline).…”

Section: Wdcm and Tdcm Location Of Workersmentioning

confidence: 99%

Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

Sleem¹,

Mekky²,

El–Sappagh³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In recent years, mobile crowdsourcing has emerged as a method to harness human power to perform spatial tasks. Reference [34] investigated the quality-aware online task assignment (QAOTA) problem in mobile crowdsourcing.…”

Section: Task Assignment In Crowdsourcingmentioning

confidence: 99%

Selecting Workers Wisely for Crowdsourcing When Copiers and Domain Experts Co-exist

Fang

Sun

et al. 2022

Future Internet

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Crowdsourcing integrates human wisdom to solve problems. Tremendous research efforts have been made in this area. However, most of them assume that workers have the same credibility in different domains and workers complete tasks independently. This leads to an inaccurate evaluation of worker credibility, hampering crowdsourcing results. To consider the impact of worker domain expertise, we adopted a vector to more accurately measure the credibility of each worker. Based on this measurement and prior task domain knowledge, we calculated fine-grained worker credibility on each given task. To avoid tasks being assigned to dependent workers who copy answers from others, we conducted copier detection via Bayesian analysis. We designed a crowdsourcing system called SWWC composed of a task assignment stage and a truth discovery stage. In the task assignment stage, we assigned tasks wisely to workers based on worker domain expertise calculation and copier removal. In the truth discovery stage, we computed the estimated truth and worker credibility by an iterative method. Then, we updated the domain expertise of workers to facilitate the upcoming task assignment. We also designed initialization algorithms to better initialize the accuracy of new workers. Theoretical analysis and experimental results showed that our method had a prominent advantage, especially under a copying situation.

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“…In Ref. [28], Miao et al point that a basic problem of mobile crowdsourcing is when both tasks and crowd workers appear in the platforms dynamically, how to assign an appropriate set of tasks to each other is a problem. However, whether the crowd workers are reliable or not is a problem worth considering.…”

Section: Mobile Crowdsourcingmentioning

confidence: 99%

“…Moreover, in Ref. [28], Miao et al found that the majority of existing studies ignored the fact that crowd workers might be unreliable, and thus, their task assignment approaches cannot ensure the overall quality. erefore, Miao et al proposed a quality-aware online task assignment approach in mobile crowdsourcing.…”

Section: Common Techniques For Mobile Crowdsourcingmentioning

confidence: 99%

Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

Liu

Zhong

Xie

et al. 2021

Mobile Information Systems

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With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research.

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Quality-aware Online Task Assignment in Mobile Crowdsourcing

Cited by 16 publications

References 42 publications

Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

Selecting Workers Wisely for Crowdsourcing When Copiers and Domain Experts Co-exist

Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

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