Real-time Distributed Mobile Crowdsensing Task Assignment Method Based on Utility Maximization
In order to solve the high-response requirement of large-scale real-time tasks in mobile crowd-sensing systems,a distributed task assignment scheme for dynamic arrival and departure of participants is studied.Firstly,global clustering is per-formed based on task distance and participant reachable distance,and a participant willingness model of Boltzmann distribution based on participant reputation is designed to characterize the quality of sensing data of participants.Secondly,in order to achieve effective incentives,considering the reputation of the participants and the budget of the platform,the participants are greedily se-lected to complete the task assignment and the remaining budget is redistributed according to the completion rate of different cluster.Finally,a participant utility function is proposed,and the sensing platform maximizes the participant's utility under budget con-straints.The advantages of the proposed algorithm on task assignment,task completion rate,utility,and remaining budget are veri-fied with a real traffic trajectory dataset.
mobile crowd sensinglarge scale task allocationreal time distributed
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