摘要：

针对时空众包在线任务分配问题,提出任务范围调节算法DMRA与基于预测分析的在线任务分配算法PAMA。DMRA算法以任务位置为中心,根据工人密度动态调整任务的范围。PAMA算法基于历史统计概率,采用贝叶斯分类器预测下一时间戳的对象分布情况,在此基础上,执行带权二分图最优匹配算法以完成任务分配。实验结果表明,将DMRA算法与PAMA算法相结合,能够提升任务分配的总效用,降低工人的差旅成本,任务分配性能优于贪心算法与随机阈值算法。

关键词: 时空众包, 在线任务分配, 分配总效用, 工人差旅成本, 贝叶斯分类预测, 统计预测

Abstract:

Aiming at online task allocation problem of spatiotemporal crowdsourcing,a task range adjustment algorithm DMRA and an online task allocation algorithm PAMA based on predictive analysis are proposed.The DMRA algorithm takes task location as the center and dynamically adjusts the range of tasks according to worker density.The PAMA algorithm uses Bayesian classifier to predict the distribution of the next timestamp object based on historical statistical probability.On this basis,the weighted bipartite graph optimal matching algorithm is executed to complete the task allocation.Experimental results show that the combination of DMRA algorithm and PAMA algorithm can improve the total utility of task allocation and reduce the travel cost of workers,and the performance of task allocation is better than that of greedy algorithm and random threshold algorithm.

Key words: spatiotemporal crowdsourcing;online task allocation, total utility of allocation, travel cost of workers, Bayesian classification prediction, statistical prediction

中图分类号: 

• TP391