基于动态效用的时空众包在线任务分配

余敦辉; 张灵莉; 付聪

doi:10.11999/JEIT170930

基于动态效用的时空众包在线任务分配

doi: 10.11999/JEIT170930

(湖北大学计算机与信息工程学院武汉 430062)

基金项目:

国家重点基础研究发展计划(2014CB340404)，国家自然科学基金(61373037, 61672387)

详细信息

作者简介:
余敦辉：男，1974年生，副教授，研究方向为服务计算、大数据. 张灵莉：女，1993年生，硕士生，研究方向为大数据. 付聪：男，1991年生，硕士生，研究方向为大数据.

中图分类号: TP393
计量
- 文章访问数: 1513
- HTML全文浏览量: 225
- PDF下载量: 60
- 被引次数: 0
出版历程
- 收稿日期: 2017-10-09
- 修回日期: 2018-04-08
- 刊出日期: 2018-07-19

Online Task Allocation of Spatial Crowdsourcing Based on Dynamic Utility

YU Dunhui ZHANG Lingli FU Cong

Funds:

The National Key Basic Research and Department Program of China (2014CB340404), The National Natural Science Foundation of China (61373037, 61672387)

摘要

摘要: 为提升众包任务在线分配的总体效用，该文提出一种适用于时空众包环境的在线任务分配方法。该方法针对时空众包环境下的在线任务分配问题，首先提出一种以众包任务为中心的K最近邻算法来进行候选众包工人的选择，进而设计一种基于动态效用的阈值选择算法，实现众包工人与任务的最优分配。实验结果显示，文中所提出算法具有较好的有效性和可行性，并能在一定程度上保证众包工人的可靠性，优化平台总效益。
- 任务分配 /
- 时空众包 /
- K最近邻算法 /
- 阈值选择算法
Abstract: In order to improve the overall effectiveness of the online assignment of crowdsourcing tasks, an online task assignment method is proposed for the space-time crowdsourcing environment. To deal with the problem of online task assignment in spatiotemporal crowdsourcing environment, a K-NearestNeighbor (KNN) algorithm is firstly proposed based on crowdsourcing task to select the candidate crowdsourcing workers. Then a threshold selection algorithm based on dynamic utility is designed to realize the optimal allocation of crowdsourcing workers and tasks. Experimental results show that the proposed algorithm is effective and feasible, and can guarantee the reliability of crowdsourcing workers and optimize the overall efficiency of the platform.
- Task assignment、Spatial crowdsourcing、K-NearestNeighbor (KNN) algorithm、Threshold selection algorithm /

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参考文献(16)

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