Considering Decoy Effect and Fairness Preference: An Incentive Mechanism for Crowdsensing

Abstract

Crowdsensing is a system which collects sensing data through recruiting a large number of participants to perform the sensing tasks. It consists of two parts: 1) the platform side and 2) the user side. The incentive mechanism is necessary to recruit a sufficient number of participants for the utility of the platform, and to guarantee the utility of the participants for the users. Current mechanisms for crowdsensing are based on traditional economics with two basic hypotheses. The first is that the preference of people is constant, which implies that their preference for an item does not change under different circumstances; the second is that the utility of people is equal to the payment they obtain. However, decoy effect from behavioral economics shows that people’s preferences change as the alternative set changes. And resulting from fairness preference theory, people’s utility depends not only on the actual income they receive but also on the fairness of the income of all people. Thus, current incentive mechanisms will lead to users’ wrong selection making. To solve these problems, we design the framework for publishing tasks based on decoy effect (FPDE) mechanism on the platform side. And we design the payoff allocation based on fairness preference (PAFP) mechanism for the user side. We compare our mechanisms with a new and advanced mechanism called incentive mechanism for crowdsensing-centric (IMCC) model. The simulations show that FPDE can increase the utility of the platform by approximately 20.8%, and the utility of the users will increases by 14.8% under PAFP.