Task Allocation Method of Mobile Crowdsensing Based on Group Collaboration
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摘要: 时空覆盖类感知任务对参与者的时间与空间约束使得传统单参与者模式难以适用。为此，该文提出群组协作的移动群智感知任务分配方法，以群组模式替代传统单参与者模式。设计层次化群组协作的任务分配框架，提出偏好感知的社交群组生成方法，引入社交关系生成社交群组，提高任务完成率。提出效用优化的任务群组匹配方法，采用网络流理论进行群组-任务匹配，保证平台效用最大化。仿真结果表明所提方法在任务完成率与平台效用方面均有较大提升。Abstract: The temporal and spatial constraints of spatio-temporal coverage tasks make it difficult to utilize the traditional single-participant model. Therefore, a task allocation method based on group collaboration in mobile crowdsensing is proposed to replace the traditional single participant mode with group mode. A task allocation framework for hierarchical group collaboration and a preference-aware social group generation method is proposed, In addition, social groups are generated by introducing social relationships to improve the task completion rate. A task-group matching method for utility optimization is proposed, and the network flow theory is used to perform group-task matching to ensure the maximum utility of the platform. Simulation results show that the proposed method can improve the task completion rate and platform utility.
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Key words:
- Mobile crowdsensing /
- Task assignment /
- Group collaboration /
- Platform utility
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表 1 实验参数
参数 值 任务接受率参数($\alpha $,$\beta $) 4, 0.5 老参与者的技能阈值($ \lambda $) 0.5 偏好超参数(${P_1}$,${P_2}$) 0.5 相关因素概率递增上限($ {I_{1\max }} \sim {I_{6\max }} $) 1.5 参与者技能的更新参数($\gamma $) 3 领导节点之间的通信成本($\vartheta $) 1 单位跳数的通信成本(${ {{\rm{unit}}} _{ki} }$) 0.5 任务的总周期($\psi $) [300, 800] 参与者在线时间窗口 [1, 10] 
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