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

Tran, Luan; To, Hien; Fan, Liyue; Shahabi, Cyrus

doi:10.1145/3078853

Cited by 59 publications

(52 citation statements)

References 46 publications

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“…[13] considers performing online task assignment while preserving the privacy of tasks and workers under the circumstance that the server is untrusted. [12,19] proposes a real-time framework for task assignment. The difference between our work and the aforementioned works is that they focus on micro tasks which can be completed by a single worker, and we study on the assignment for specialty-aware tasks which have requirements on skills of workers and usually have to be completed by multiple workers collaboratively.…”

Section: Task Assignment In Spatial Crowdsourcingmentioning

confidence: 99%

Specialty-Aware Task Assignment in Spatial Crowdsourcing

Song

Zhu

2018

Artificial Intelligence and Symbolic Computation

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With the rapid development of Mobile Internet, spatial crowdsourcing is gaining more and more attention from both academia and industry. In spatial crowdsourcing, spatial tasks are sent to workers based on their locations. A wide kind of tasks in spatial crowdsourcing are specialty-aware, which are complex and need to be completed by workers with different skills collaboratively. Existing studies on specialty-aware spatial crowdsourcing assume that each worker has a united charge when performing different tasks, no matter how many skills of her/him are used to complete the task, which is not fair and practical. In this paper, we study the problem of specialty-aware task assignment in spatial crowdsourcing, where each worker has fine-grained charge for each of their skills, and the goal is to maximize the total number of completed tasks based on tasks' budget and requirements on particular skills. The problem is proven to be NP-hard. Thus, we propose two efficient heuristics to solve the problem. Experiments on both synthetic and real datasets demonstrate the effectiveness and efficiency of our solutions.

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Section: Task Assignment In Spatial Crowdsourcingmentioning

confidence: 99%

Specialty-Aware Task Assignment in Spatial Crowdsourcing

Song

Zhu

2018

Artificial Intelligence and Symbolic Computation

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“…Thus, the paradigm of information systems is changing from the existing service center to the data center. As real-time data and data analysis have become important in the data-driven paradigm, various methods of data collection have been studied in recent research [1].…”

Section: Introductionmentioning

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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“…location [3]. For example, in a disaster search and rescue scenario, the requester urgently needs to collect images and videos of search areas from different locations in a country [4]. The requester submits a query to a mobile crowdsourcing server.…”

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confidence: 99%

“…Mobile crowdsourcing systems must assign emergency tasks to workers who are in the vicinity to the target location. Most tasks must be accomplished within a set time, making it impossible for mobile workers to travel long distances to accomplish the required tasks [4].…”

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A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing

Sun

Tan

2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionWith the advancing technology of mobile devices with numerous built-in sensors, mobile crowdsourcing has recently emerged as a new collaboration paradigm in numerous intelligent mobile information systems [1]. The existing mobile crowdsourcing has applications in numerous domains including urban planning, traffic monitoring, ride sharing, environmental monitoring and intelligent disaster response [2]. Mobile crowdsourcing is a combination of spatial crowdsourcing and smart phone technology that employs mobile workers to perform certain tasks in a specific AbstractMobile crowdsourcing has emerged as a promising collaboration paradigm in which each spatial task requires a set of mobile workers in near vicinity to the target location. Considering the desired privacy of the participating mobile devices, trust is considered to be an important factor to enable effective collaboration in mobile crowdsourcing. The main impediment to the success of mobile crowdsourcing is the allocation of trustworthy mobile workers to nearby spatial tasks for collaboration. This process becomes substantially more challenging for large-scale online spatial task allocations in uncertain mobile crowdsourcing systems. The uncertainty can mislead the task allocation, resulting in performance degradation. Moreover, the large-scale nature of real-world crowdsourcing poses a considerable challenge to spatial task allocation in uncertain environments. To address the aforementioned challenges, first, an optimization problem of mobile crowdsourcing task allocation is formulated to maximize the trustworthiness of workers and minimize movement distance costs. Second, for the uncertain crowdsourcing scenario, a Markov decision process-based mobile crowdsourcing model (MCMDP) is formulated to illustrate the dynamic trust-aware task allocation problem. Third, to solve large-scale MCMDP problems in a stable manner, this study proposes an improved deep Q-learning-based trust-aware task allocation (ImprovedDQL-TTA) algorithm that combines trust-aware task allocation and deep Q-learning as an improvement over the uncertain mobile crowdsourcing systems. Finally, experimental results illustrate that the ImprovedDQL-TTA algorithm can stably converge in a number of training iterations. Compared with the reference algorithm, our proposed algorithm achieves effective solutions on the experimental data sets. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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

Cited by 59 publications

References 46 publications

Specialty-Aware Task Assignment in Spatial Crowdsourcing

Specialty-Aware Task Assignment in Spatial Crowdsourcing

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

A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing

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