Abstract
With the rapid development of mobile networks and the proliferation of mobile devices, Spatial Crowdsourcing (SC) has attracted the interest of industry and research groups. In addition to considering the specific spatial constraints in the existing research spatial crowdsourcing, each task has an effective duration, operational complexity, number of workers required, and incentive budget constraints. In this scenario, we studied the MQC-TA (Maximum Quality and Minimum Cost Task Assignment) problem. Firstly, the worker incentive model is established. The MQC-GAC algorithm is designed according to the MQC-TA problem to maximize the task completion quality and minimize the incentive budget. The algorithm combined the fast convergence of Genetic Algorithm and the positive feedback mechanism of Ant Colony Optimization Algorithm. Finally, the effectiveness and efficiency of the proposed method are verified by a comprehensive experiment on the data set.
Highlights
In recent years, crowdsourcing has been widely used in business, such as the establishment and application of Amazon Mechanical Turk, crowdflower, crowdcloud and microworkers platforms
The MQC-TA problem studied in this paper aims to solve the optimal matching problem between effective spatial tasks and online workers in a task assignment cycle by using MQC-GAC algorithm, so as to maximize task completion quality and minimize incentive expectations
The following is an experimental analysis of proposed mechanism and algorithm for solveing MQC-TA problem from different perspectives
Summary
In recent years, crowdsourcing has been widely used in business, such as the establishment and application of Amazon Mechanical Turk, crowdflower, crowdcloud and microworkers platforms. At the same time, crowdsourcing is popular in image processing [1], database [2], NLP [3] and other research fields. In the spatial crowdsourcing environment, workers need to arrive at a specific work place to perform tasks. Such spatiotemporal data are closely related, such as real-time special vehicle service platform: Didi travel, where Didi users are task requesters, Didi special vehicle is workers, workers need to move to the location of Didi users to pick up passengers to the destination. In view of the task assignment problem in the spatial crowdsourcing environment, most of the existing researches
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