时空众包中基于质量感知的在线激励机制

Online incentive mechanism based on quality perception in spatio-temporal crowdsourcing

在实时、复杂的网络环境中,如何激励工人参与任务并得到高质量的感知数据是时空众包研究的重点。基于此,提出一种基于质量感知的时空众包在线激励机制。首先,为了适应时空众包实时性的特点,提出一种阶段性在线选择工人算法(POA),该算法在预算约束下将整个众包活动周期分为多个阶段,每个阶段在线选择工人;其次,为了提高质量预估的精度与效率,提出一种改进的最大期望(IEM)算法,该算法在算法迭代的过程中优先考虑可信度高的工人提交的任务结果;最后,通过真实数据集上的对比实验,验证了所提激励机制在提高平台效用方面的有效性。实验结果表明,POA相较于改进的两阶段拍卖(ITA)算法、多属性与两阶段相结合的拍卖(M-ITA)算法,以及L-VCG(Lyapunov-based Vickrey-Clarke-Groves)等拍卖算法,效率平均提高了11.11%,工人的额外奖励金额平均提升了12.12%,可以激励工人向冷门偏远地区移动;在质量预估方面,IEM算法相比其他质量预估算法,在精度和效率上分别平均提高了5.06%和14.2%。

In the real-time and complex network environment,how to motivate workers to participate in tasks and obtain high-quality perception data is the focus of spatio-temporal crowdsourcing research.Based on this,a spatio-temporal crowdsourcing’s online incentive mechanism based on quality perception was proposed.Firstly,in order to adapt to the realtime characteristics of spatio-temporal crowdsourcing,a Phased Online selection of workers Algorithm(POA)was proposed.In this algorithm,the entire crowdsourcing activity cycle was divided into multiple stages under budget constraints,and workers were selected online in each stage.Secondly,in order to improve the accuracy and efficiency of quality prediction,an Improved Expected Maximum(IEM)algorithm was proposed.In this algorithm,the task results submitted by workers with high reliability were given priority in the process of algorithm iteration.Finally,the effectiveness of the proposed incentive mechanism in improving platform utility was verified by comparison experiments on real datasets.Experimental results show that in terms of efficiency,compared with the Improved Two-stage Auction(ITA)algorithm,the Multi-attribute and ITA(M-ITA)algorithm,Lyapunov-based Vickrey-Clarke-Groves(L-VCG)and other auction algorithms,the efficiency of POA has increased by 11.11% on average,and the amount of additional rewards for workers has increased by 12.12% on average,which can encourage workers to move to remote and unpopular areas;In terms of quality estimation,the IEM algorithm has an average improvement of 5.06% in accuracy and 14.2% in efficiency compared to other quality estimation algorithms.