“Follower of the Reference Point”: Platform Utility-Oriented Incentive Mechanism in Crowdsensing

Abstract

Crowdsensing uses the sensing units of many participants with idle resources to collect data. Since the budget of the platform is limited, it is crucial to design a mechanism to motivate participants to lower their bids. Current incentive mechanisms assume that participants’ gains and losses are absolute, but behavioral economics shows that a certain reference point determines participants’ gains and losses. Reference dependence theory shows that the reference reward given by a platform and the reward obtained before will greatly affect the decision-making of the participant. Therefore, this paper proposes a participants’ decision-making mechanism based on the reference dependence theory. We set a reference point to reduce the participants’ bids, improving the platform’s utility. At the same time, risk preference reversal theory shows that participants evaluate the benefits based on the relative value of the rewards rather than the absolute value. Therefore, this paper proposes a winner selection mechanism based on the risk preference reversal theory. Theoretical analysis and simulations demonstrate that this paper’s incentive mechanism can guarantee the platform’s utility and improve the task completion rate.