Location-dependent Task Allocation Mechanism In Two Perspectives Of Mobile Crowd-sensing

As one of the research hot spots in recent years,mobile Crowd-Sensing is more and more concerned with the task assignment and user scheduling problems in this context.The main purpose of task assignment is to properly match the tasks to the corresponding participants.Then participants work together to accomplish large and complex tasks in a collaborative manner.Therefore,it is important to provide an efficient task assignment mechanism.At present,the research work on task assignment and user scheduling is mainly divided into two perspectives: participants and platforms.In the perspective of the platform,the existing task allocation mechanism usually only considers the location of the participant's current moment,ignoring the participant's position in the future and the problem of random movement.From the perspective of the participants,the related work has been studied from the location and time of the perceived task.Without considering the order in which participants complete tasks,it also indirectly affects the efficiency of perceived work.In view of the above problems,this paper proposes an emerging task allocation mechanism in two different perspectives: platform and participants.In the platform perspective: this paper uses the Markov model to analyze and predict the location of the participants in the future,and proposes a platform efficiency maximization task allocation algorithm to solve the task assignment problem.From the perspective of participants: This paper improves the ant colony algorithm and proposes the participant efficiency maximization algorithm.Combining the three factors of distance,time and reward,the participant plans a reasonable task completion order path.Finally,we design and implement a task allocation system,which uses two task allocation mechanisms to enable users and readers to have a deeper understanding of the meaning of the proposed algorithm.