A Reputation-Based Multi-User Task Selection Incentive Mechanism for Crowdsensing

Crowdsensing high quality data relies on the efficient participation of users. However, the existing incentive mechanism is unable to take into account the dual requirements of both quantity and quality of users' participation. In this paper, we propose Crowdsensing Task Selection algorithm and rewards allocation incentive mechanism based on Reputation Evaluation model(CTSRE), which deploys the reputation weighted rewards allocation method to effectively encourage users to actively participate in the execution of tasks. In CTSRE, we adopt a game-theoretic approach and apply best response dynamics based algorithm to achieve the goal of maximizing users' utilities. We show that the task selection algorithm can converge in finite time and meet the fairness requirement. We also design a reputation conversion method and updating rule to improve incentive and fairness of the mechanism. Through numerical experiments and comparative analysis, we verify that the task selection algorithm meets the convergence requirements. The application of sigmoid function for reputation conversion improves the fairness of rewards allocation and motivate users to improve their reputation to obtain high rewards. Experimental results indicate that CTSRE can effectively ensure the quantity and the quality of users' participation.