Reliable diversity-based spatial crowdsourcing by moving workers

Cheng, Peiyao; Lian, Xiang; Chen, Zhao; Fu, Rui; Chen, Lei; Han, Jinsong; Zhao, Jizhong

doi:10.14778/2794367.2794372

Cited by 139 publications

(129 citation statements)

References 16 publications

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“…The literature in spatial crowdsourcing presents many different ways to assign tasks to workers, for instance, maximizing the number of assigned tasks, [11,14], maximizing a given matching score [5,13,16,17] or minimizing the total amount of reward paid out by requesters, while maximizing the number of assignments [6].…”

Section: Related Workmentioning

confidence: 99%

“…All of the works above focus on either optimizing a single criterion or on also optimizing a secondary criterion, which serve as a tie-breaker. On the other hand, Cheng et al [5] study the reliable diversity-based spatial crowdsourcing (RDB-SC) problem, which assigns workers to tasks such that tasks can be accomplished with high reliability and spatial/temporal diversity. Thus, there are two criteria to be optimized simultaneously: reliability and diversity.…”

Section: Related Workmentioning

confidence: 99%

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In-route task selection in crowdsourcing

Costa

Nascimento

2018

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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One important problem in crowdsourcing is that of assigning tasks to workers. We consider a scenario where a worker is traveling on a preferred/typical path (e.g., from school to home) and there is a set of tasks available to be performed. Furthermore, we assume that: each task yields a positive reward, the worker has the skills necessary to perform all available tasks and he/she is willing to possibly deviate from his/her preferred path as long as he/she travels at most a total given distance/time. We call this problem the In-Route Task Selection (IRTS) problem and investigate it using the skyline paradigm in order to obtain the exact set of non-dominated solutions, i.e., good and diverse solutions yielding different combinations of smaller or larger rewards while traveling more or less. This is a practically relevant problem as it empowers the worker as he/she can decide, in real time, which tasks suit his/her needs and/or availability better. After showing that the IRTS problem is NP-hard, we propose an exact (but expensive) solution and a few others practical heuristic solutions. While the exact solution is suitable only for reasonably small IRTS instances, the heuristic solutions can produce solutions with good values of precision and recall for problems of realistic sizes within practical, in fact most often sub-second, query processing time.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

In-route task selection in crowdsourcing

Costa

Nascimento

2018

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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show abstract

“…The DRR algorithm computes all distances between participants and target locations online, which can increase computing costs. This method does not consider the participants' location privacy during crowdsourcing [13,20].…”

Section: Related Workmentioning

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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“…Several variants of the problem of assigning spatial tasks to mobile workers have been been studied under various sets of hypothesis [11,7,12,5,10,16].…”

Section: Related Workmentioning

confidence: 99%

“…They studied the problem of maximizing the number of assigned tasks while ensuring that the quality of the answers reaches a confidence level. In [5], Chen et al consider the assignment of moving workers to spatio-temporal tasks. Tasks can only be accomplished within a specified area and time interval.…”

Section: Related Workmentioning

confidence: 99%

Cost Minimization and Social Fairness for Spatial Crowdsourcing Tasks

Liu¹,

Abdessalem²,

Wu³

et al. 2016

Database Systems for Advanced Applications

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Spatial crowdsourcing is an activity consisting in outsourcing spatial tasks to a community of online, yet on-ground and mobile, workers. A spatial task is characterized by the requirement that workers must move from their current location to a specified location to accomplish the task. We study the assignment of spatial tasks to workers. A sequence of sets of spatial tasks is assigned to workers as they arrive. We want to minimize the cost incurred by the movement of the workers to perform the tasks. In the meanwhile, we are seeking solutions that are socially fair. We discuss the competitiveness in terms of competitive ratio and social fairness of the Work Function Algorithm, the Greedy Algorithm, and the Randomized versions of the Greedy Algorithm to solve this problem. These online algorithms are memory-less and are either inefficient or unfair. In this paper, we devise two Distribution Aware Algorithms that utilize the distribution information of the tasks and that assign tasks to workers on the basis of the learned distribution. With realistic and synthetic datasets, we empirically and comparatively evaluate the performance of the three baseline and two Distribution Aware Algorithms.

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Reliable diversity-based spatial crowdsourcing by moving workers

Cited by 139 publications

References 16 publications

In-route task selection in crowdsourcing

In-route task selection in crowdsourcing

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

Cost Minimization and Social Fairness for Spatial Crowdsourcing Tasks

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