The Game Selection Model for the Payoff Strategy Optimization of Mobile CrowdSensing Task

• Guosheng Zhao,
• Dongmei Liu,
• Jian Wang,

Vol. 15, No. 4, April 30, 2021
10.3837/tiis.2021.04.013, Download Paper (Free):

• Mobile Crowdsensing
• Stochastic Evolutionary Game
• data quality
• Credibility
• Payoff Selection

Abstract

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

Statistics

Cite this article