The Game Selection Model For The Payoff Optimization Of Mobile CrowdSensing Task

Mobile Crowd Sensing(MCS)refers to the use of portable intelligent mobile devices to collect and share sensing information and data.In the stage of publishing and completing perception tasks,task publishers hope to get perception data at a relatively low cost,while users hope to get higher payoff in completing perception tasks.However,the popularity of smartphones has accelerated the widespread use of MCS,but the presence of malicious and low-quality users will lead to a reduction in the effectiveness of the MCS,the existing research lacks to optimize the payoff of task publishers and users under the premise of ensuring data quality.To optimize the revenue of task publishers and users,the purpose is to encourage task publishers to publish perception tasks and encourage users to actively participate in completing perception tasks.And the purpose of evaluating the data quality of users is to improve the fitness of perception to match the target users,so that task publishers and users can get the optimal payoff.Aiming at the optimal payoff problem of task publisher and user,researchers have built various Mobile Crowd Sensing payoff models,but most models are based on completely rational foundation.This paper studies two payoff optimization selection models to solve the payoff game problem between task publishers and users.This paper mainly studies the following two aspects:(1)Aiming at the traditional payoff game problem,which is based on the complete rationality of task publishers and users,this paper proposes a payoff optimization model based on evolutionary game.Firstly,the payoff optimization process is modeled as a task publisher-user evolutionary game model.Then,the low-quality data is identified by the data quality evaluation algorithm,which improves the fitness of perception to match the target users,so that the task publisher and users can obtain the optimal payoff at the current time.Finally,by solving the evolutionary stability strategy and analyzing the stability of the model,the optimal payoff strategies of both sides in different initial states are obtained.(2)In view of the randomness of strategy selection between task publishers and users,this paper proposes a payoff optimization selection model based on random game.Based on the concept of Gaussian white noise,the nonlinear Itó stochastic differential equation is introduced to construct the task publisher user stochastic game model.The stochastic differential equations between task publisher and user are solved by Taylor expansion.According to the stability judgment theorem of stochastic differential equation,the stability of the strategy selection of task publisher and user is analyzed.Finally,the optimal payoff strategies of both sides under different intensity of random interference and different initial states are obtained.