Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing

Abstract

Encouraging a certain number of users to participate in a sensing task continuously for collecting high-quality sensing data under a certain budget is a new challenge in the mobile crowdsensing. The users’ historical reputation reflects their past performance in completing sensing tasks, and users with high historical reputation have outstanding performance in historical tasks. Therefore, this study proposes a reputation constraint incentive mechanism algorithm based on the Stackelberg game to solve the abovementioned problem. First, the user’s historical reputation is applied to select some trusted users for collecting high-quality sensing data. Then, the two-stage Stackelberg game is used to analyze the user’s resource contribution level in the sensing task and the optimal incentive mechanism of the server platform. The existence and uniqueness of Stackelberg equilibrium are verified by determining the user’s optimal response strategy. Finally, two conversion methods of the user’s total payoff are proposed to ensure flexible application of the user’s payoff in the mobile crowdsensing network. Simulation experiments show that the historical reputation of selected trusted users is higher than that of randomly selected users, and the server platform and users have good utility.

Incentive techniques for the Internet of Things: A survey

“…To collect high-quality of sensing data within the specified budget, it is essential to choose optimal users/volunteers for MCS. The accuracy of the sensing data depends on the coverage of mobile users in the target area and also on the previous reputation of the mobile users [82], [83]. The work in [84] considered these factors for incentivizing the participants using Stackelberg game theory.…”