Completely-competitive-equilibrium-based crowdsensing pricing mechanism

Crowdsensing accomplishes extended general and complex social sensing tasks through allocating tasks to a large number of ordinary users (or workers), and has attracted extensive attention in recent years. How to motivate users to participate in sensing tasks is one of the most important issues in crowdsensing. However, the existing incentive mechanisms mainly focus on how to set prices to enable users to submit high-quality sensing data,ignoring the problem of blind quotes, which can easily lead to the imbalance of the number of users participating in the task execution, so that the platform cannot obtain the optimal revenue. To tackle this challenge, a completely-competitive-equilibriumcrowdsensing pricing mechanism is proposed. Firstly, the multi-player game between platform and users is abstracted as a two-person game between the platform and the market. Then the market type probability is introduced and the two-person incomplete information game is transformed into the two-person complete imperfect information game through Harsanyi transformation. Finally, through multiple rounds of repeated games on the platform, the platform′s price converged to completely competitive equilibrium. Theoretical analysis and experimental results show that the proposed incentive mechanism can achieve completely competitive equilibrium.