移动群智感知中时间窗口相关的参与者选择机制

目前许多移动群智感知应用要求参与者收集一段时间内连续的感知数据，而现有研究在这方面却考虑不足。针对上述应用场景提出了时间窗口相关的参与者选择机制，主要包括基于动态规划算法设计了一种时间窗口相关的参与者选择方法，目标为覆盖任务时间段的同时最大化数据效益；参与者信誉值更新机制，根据参与者参与任务的意愿程度和数据质量更新参与者的信誉值。最后通过仿真实验与两种普遍应用的参与者选择方法作比较，实验证明所提出的参与者选择机制在数据可靠性、数据效益和感知成本等方面具有更好的效果，因此所提出的参与者选择机制在时间窗口相关的任务中有更好的应用前景。

Participant selection mechanism for time window dependent tasks in MCS

In many applications of mobile crowd sensing, participants should collect continuous data over a period of time in this scenario on which existing research lacks consideration. For this scenario, this paper proposed a participant selection mechanism which was time window dependent. This mechanism included a participant selection method which was time window dependent based on dynamic programming algorithm. The target of the method was to maximize data benefits while cover time period of the task. It gave an updating mechanism of participant''s reputation based on the willingness and data quality of the participant. Finally, simulation results show that compared with two common selection mechanisms, the proposed participant selection mechanism has better performance in terms of data reliability, data benefits and cost and has better prospects.