Incentive mechanism for crowdsensing platforms based on multi-leader stackelberg game

Abstract

Nowadays, the exponential growth of smartphones creates a potential paradigm of mobile crowdsensing. A sensing task originator accomplishes its sensing data collection work by publishing them on crowdsensing platforms. All the platforms want to attract the task originator to use their services in order to make higher profit. Thus, the issue of competition arises. In this paper, we study the incentive mechanism based on pricing strategy for crowdsensing platforms. We formulate the competition among platforms as a dynamic non-cooperative game and use a multi-leader Stackelberg game model, where platforms are leaders and the task originator is the follower. In the real world, it is difficult for a platform to know the strategies of others. So we propose an iterative learning algorithm to compute its Nash equilibrium. The iterative learning algorithm is that each platform learns from its historic strategy and the originator’s response. Through extensive simulations, we evaluate the performance of our incentive mechanism.