A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

Abstract

Crowdsensing uses the participants’ smart devices to form a new perception network. The coverage of crowdsensing’s tasks determines the quality of services. Under the constraint of budget and the number of participants, the platform needs to increase the participation duration of participants through incentive mechanisms to increase the coverage of tasks. There are two problems with the existing incentive mechanisms: (1) many incentives ignore the participants’ characteristics, and using a single incentive mechanism for different participants will make the incentive effect not reach the expectation; (2) many incentives will affect the effectiveness because of the decision problem caused by asymmetric information. Inspired by ensemble learning and prospect theory, this paper proposes the Incentive Mechanism based on Ensemble Learning and Prospect Theory (IMELPT). First, we propose the Deep-Stacking-Generation algorithm based on Dropout (DSGD), to predict the participants and distinguish whether they are long-term or short-term participants. If the participants are short-term, we incentivize them through the Short-term Participant Incentive Mechanism based on Prospect Theory (SPIMPT). We increase the participation duration by transforming the change in reward into asymmetric information that aligns the participant’s goal with the platform. If the participants are long-term participants, we motivate them through the Long-term Participant Incentive Mechanism (LPIM), to maintain the participation rate of participants by maximizing their utility. Theoretical analysis and experiments on real datasets demonstrated that IMELPT can reliably improve the coverage of crowdsensing tasks.