基于动态效用的时空众包在线任务分配  

Online Task Allocation of Spatial Crowdsourcing Based on Dynamic Utility

作　　者：余敦辉 张灵莉 付聪 YU Dunhui;ZHANG Lingli;FU Cong(School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, Chin)

机构地区：[1]湖北大学计算机与信息工程学院,武汉430062

出　　处：《电子与信息学报》2018年第7期1699-1706,共8页Journal of Electronics & Information Technology

基　　金：国家重点基础研究发展计划(2014CB340404);国家自然科学基金(61373037;61672387)~~

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。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 crowdsoureing workers and optimize the overall efficiency of the platform.

关 键 词：任务分配 时空众包 K最近邻算法 阈值选择算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]