移动群智感知中基于社区的任务分发算法

Task distribution algorithm based on community in mobile crowd sensing

针对移动群智感知(MCS)任务分发的有效性和精准性问题,提出了基于社区的任务分发算法。为了解决所提问题,该方法首先通过计算移动节点间的最小生成树、连接参量、社区融合度,抽象和识别出节点的行为模式,将用户合理划分成不同的社区,然后计算感知任务与社区行为模式特征值的匹配度,根据匹配度由社区的中心节点完成任务的分发。仿真结果表明,与其他算法相比,所提方法有效地提高了任务分发的精确性和任务完成率,节约了任务完成的时间成本。

A community-based task distribution algorithm was proposed to solve the problem of the validity of mobile crowd sensing (MCS) task distribution.By calculating the minimum spanning tree (MST),the connection parameter (CP) and the community convergence degree (CI) between the mobile nodes,the behavior patterns of the users were abstracted and identified to rationally divide the nodes into different communities.Then,the eigenvalue matching degree of the community behavior patterns with the sensing task was calculated.According to the matching degree,the distribution of the corresponding tasks was completed by the central node of the community.The simulation results show that the proposed method can effectively improve the accuracy of the task distribution and the task completion rate,and save the time cost of the task completion.