Efficient task assignment for spatial crowdsourcing: A combinatorial fractional optimization approach with semi-bandit learning

Hassan, Umair ul; Curry, Edward

doi:10.1016/j.eswa.2016.03.022

Cited by 75 publications

(40 citation statements)

References 46 publications

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“…Moreover, by combining several participants' information, fraudulent users can easily guess a specific participant's approximate location. Recent studies that are closely related to ours include those by Ul Hassan and Curry [14] and Tong, She, Ding, Wang, and Chen [15]. Both studies consider the online spatial task assignment problem; however, they differ from our work in terms of their problems and objectives.…”

Section: Related Workmentioning

confidence: 68%

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Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

Shin

2017

Symmetry

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With the development of sensor technology and the popularization of the data-driven service paradigm, spatial crowdsourcing systems have become an important way of collecting map-based location data. However, large-scale task management and location privacy are important factors for participants in spatial crowdsourcing. In this paper, we propose the use of an R-tree spatial cloaking-based task-assignment method for large-scale spatial crowdsourcing. We use an estimated R-tree based on the requested crowdsourcing tasks to reduce the crowdsourcing server-side inserting cost and enable the scalability. By using Minimum Bounding Rectangle (MBR)-based spatial anonymous data without exact position data, this method preserves the location privacy of participants in a simple way. In our experiment, we showed that our proposed method is faster than the current method, and is very efficient when the scale is increased.

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Section: Related Workmentioning

confidence: 68%

“…This method uses a round area for spatial cloaking and point data, and thus could increase the computing cost from the server side. Ul Hassan and Curry [14] studied the combinatorial fractional optimization approach for efficient task assignment. They used the semi-bandit learning method to reduce participants' travel costs [19].…”

Section: Related Workmentioning

confidence: 99%

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

Shin

2017

Symmetry

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“…There have also been efforts to define declarative approaches for introducing crowd-based operations in data processing systems [19,20]. More recent research proposals have tried to crowdsource spatial tasks [21,22]. However, none of these works have considered the use of crowd-based operators in data processing for streams or events.…”

Section: Related Workmentioning

confidence: 99%

The Event Crowd

Yadav

Hassan

Hasan

et al. 2017

Proceedings of the 11th ACM International Conference on Distributed and Event-Based Systems

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Event processing systems involve the processing of high volume and variety data which has inherent uncertainties like incomplete event streams, imprecise event recognition etc. With the emergence of crowdsourcing platforms, the performance of event processing systems can be enhanced by including 'human-in-theloop' to leverage their cognitive ability. The resulting crowdsourced event processing can cater to the problem of event uncertainty and veracity by using humans to verify the results.This paper introduces the first hybrid crowd-enabled event processing engine. The paper proposes a list of five event crowd operators that are domain and language independent and can be used by any event processing framework. These operators encapsulate the complexities to deal with crowd workers and allow developers to define an event-crowd hybrid workflow. The operators are: Annotate, Rank, Verify, Rate, and Match. The paper presents a proof of concept of event crowd operators, schedulers, poolers, aggregators in an event processing system. The paper demonstrates the implementation of these operators and simulates the system with various performance metrics. The experimental evaluation shows that throughput of the system was 7.86 events per second with average latency of 7.16 seconds for 100 crowd workers. Finally, the paper concludes with avenues for future research in crowd-enabled event processing.

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“…In literature [3] [4], it reviews the development of crowdsourcing and introduces the three aspects of incentive design, task assignment and quality control. In the tasks assignment part, emphasis assigning tasks depending on the ability, time and other factors.…”

Section: Related Workmentioning

confidence: 99%

On Collaborative Planning for Time-constrained Multi-agent System

Zhu²,

Teng³

2017

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017

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Existing Multi-Agent System (MAS) pursue a high degree of concurrency, to improve the efficiency of the task on the study while neglecting the communication and time restriction from bidders. In this paper, we design a time-constrained MAS with inflexible schedule. Tenderer propose tenders to bidders and encourage bidders propose more choices and real ability. For all plan sequences, we check them and finally find out the best solution with shortest deadline or max total revenue.

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Efficient task assignment for spatial crowdsourcing: A combinatorial fractional optimization approach with semi-bandit learning

Cited by 75 publications

References 46 publications

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

The Event Crowd

On Collaborative Planning for Time-constrained Multi-agent System

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