Task Scheduling and Incentive Mechanism in Vehicular Crowdsensing: From Individual and Bounded Rationality Perspectives

With the continuous advancement of transportation systems, leveraging Vehicular Crowdsensing (VCS) for data collection and analysis within digital cities has become a promising paradigm. Most existing research focuses on improving task completion rates using location information but often overlooks the impact of drivers’ rational decision-making processes. To address this issue, we propose novel task scheduling and incentive mechanisms grounded in two distinct rational decision-making models. Specifically, drivers are categorized as Individual Rationality and Bounded Rationality based on their sensitivity to utility and cost. For drivers with Individual Rationality, who only accept tasks that ensure non-negative utility, we formulate the maximum weighted subset coverage problem (MWSC-Problem). On the other hand, for drivers exhibiting Bounded Rationality, who accept tasks with a certain probability influenced by their sensing utility and cost, we introduce the maximum probability task coverage problem (MPTC-Problem). The driver recruitment problem in both scenarios is proven to be NP-hard. For each case, we design customized scheduling and incentive algorithms to optimize both the platform’s task completion rate and cost efficiency. Meanwhile, the performance bounds and computational complexity of the proposed algorithms are theoretically analyzed. By extensive simulations on a real-world taxi dataset, the effectiveness of our strategies is validated.