Task selection in spatial crowdsourcing from worker’s perspective

Deng, Dingxiong; Shahabi, Cyrus; Demiryurek, Ugur; Zhu, Linhong

doi:10.1007/s10707-016-0251-4

Cited by 55 publications

(67 citation statements)

References 33 publications

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“…In Figure 1, the SC-server is likely to activate worker D and either worker B or C for the two tasks, respectively, without knowing that a more favorable worker A is qualified for both tasks and will arrive in the near future. Previous heuristics in the literature [Kazemi and Shahabi 2012;Deng et al 2016;Guo et al 2016] do not consider the vicinity of tasks in space and time or the budget, thus cannot be applied to Hyperlocal SC.…”

Section: Sc-server Workersmentioning

confidence: 99%

“…A recent study distinguishes SC from related fields, including generic crowdsourcing, participatory sensing, volunteered geographic information, and online matching. Research efforts in SC have focused on different aspects, such as task assignment (e.g., [Kazemi and Shahabi 2012;Tong et al 2016;Liu et al 2016b;Hu et al 2016]), task scheduling (e.g., [Deng et al 2016;Li et al 2015;Sales Fonteles et al 2016]), quality control and trust (e.g., [Kazemi et al 2013;Cheng et al 2015]), privacy (e.g., Wang et al 2017]), incentive mechanism (e.g., [Gao et al 2015;Kandappu et al 2016;). The authors in [Kazemi and Shahabi 2012] proposed task assignment problem whose goal is to maximize the number of assigned tasks.…”

Section: Related Workmentioning

confidence: 99%

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A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

Tran

Fan

et al. 2018

ACM Trans. Intell. Syst. Technol.

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Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in urban environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks. We introduce a taxonomy of several problem variants, such as budget-per-time-period vs. budget-per-campaign and binary-utility vs. distance-based-utility. We study the hardness of the task assignment problem in the offline setting and propose online heuristics which exploits the spatial and temporal knowledge acquired over time. Our experiments are conducted with spatial crowdsourcing workloads generated by the SCAWG tool and extensive results show the effectiveness and efficiency of our proposed solutions.

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Section: Sc-server Workersmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

Tran

Fan

et al. 2018

ACM Trans. Intell. Syst. Technol.

Self Cite

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“…Kazemi and Shahabi [1] proposed several heuristics to maximize the overall task assignment while conforming to the constraints of workers. Similarly, Deng et al [2] devised both exact and approximation algorithms to find a schedule for a worker such that the number of performed tasks by the worker is maximized. Spatial-temporal diversity and reliability are also taken into account in the course of task assignment.…”

Section: Related Workmentioning

confidence: 99%

“…Task assignment in SC has been extensively studied as an optimization problem recently. Given some temporal, budget, or skill constraints, these studies try to maximize, for example, the number of assigned tasks on the server side [1] , the number of workers' self-selected tasks on the client side [2] , the reliability-and-diversity score of assignment [3] , and the budget-minus-cost score of assignment [4] . However, all these studies fail to protect the privacy of either workers or task requesters.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Task Assignment in Spatial Crowdsourcing

Liu

et al. 2017

J. Comput. Sci. Technol.

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With the progress of mobile devices and wireless networks, spatial crowdsourcing (SC) is emerging as a promising approach for problem solving. In SC, spatial tasks are assigned to and performed by a set of human workers. To enable effective task assignment, however, both workers and task requesters are required to disclose their locations to untrusted SC systems. In this paper, we study the problem of assigning workers to tasks in a way that location privacy for both workers and task requesters is preserved. We first combine the Paillier cryptosystem with Yao's garbled circuits to construct a secure protocol that assigns the nearest worker to a task. Considering that this protocol cannot scale to a large number of workers, we then make use of Geohash, a hierarchical spatial index to design a more efficient protocol that can securely find approximate nearest workers. We theoretically show that these two protocols are secure against semi-honest adversaries. Through extensive experiments on two real-world datasets, we demonstrate the efficiency and effectiveness of our protocols.

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“…For example, real-time spatial crowdsourcing data can be used for online monitoring systems for end users, and historical spatial crowdsourcing data can be used for various types of map-based analytics services [29,30]. To support these types of map-based services, we must transform spatial task results into location-based report data as quickly as possible.…”

Section: Spatial Task Management Processmentioning

confidence: 99%

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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Task selection in spatial crowdsourcing from worker’s perspective

Cited by 55 publications

References 33 publications

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

Privacy-Preserving Task Assignment in Spatial Crowdsourcing

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

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