Reputation-Based Sensing Data Collection in Vehicular Crowdsensing: A Hybrid Incentive Approach

Data collection and distribution through crowdsensing has become an emerging trend in smart city scenarios. By leveraging existing vehicle resources without deploying dedicated infrastructure, Vehicular CrowdSensing (VCS) provides low-cost and high-mobility data collection on road networks. Typically, the Crowdsensing Platform (CP) issues data collection tasks, recruits Sensing Vehicles (SVs) to complete tasks, and sells the collected data to Data Demanders (DDs). Here, the goal of CP is to maximize profits through data collection and sales, and the goal of DDs is to improve satisfaction by purchasing high-quality sensing data. It can be seen that both CP and DD hope that SVs can complete more sensing tasks at a limited cost (high efficiency) while ensuring the accuracy of data collection (high quality). However, due to individual rationality and selfishness, not all SVs are willing to complete the sensing task. Therefore, how to motivate SVs to complete sensing tasks with high quality and efficiency, while handling the relationship among CP, DDs, and SVs, is a problem that needs to be considered. To solve the above problems, this paper proposes a Reputation-based Hybrid Incentive Approach (RHIA), with the goal of maximizing the utility of CP, SVs, and DDs. Specifically, in order to improve the task completion quality of SVs, we introduce vehicle reputation to measure SVs. Then, we propose a one-to-one bargaining game between CP and each SV, and use the reputation value as the sequential basis of the game. Meanwhile, in order to improve the task completion efficiency of SVs, we also design a unique SV Trajectory Planning Algorithm (STPA). Further, in order to meet the needs of DDs, a one-to- multi Stackelberg game between CP and DDs is proposed. Here, the existence and uniqueness of Nash equilibrium is proved through backward induction. Finally, based on real-world datasets, the effectiveness of our proposed RHIA and STPA is verified. Our proposed method can ensure the long-term stability of the VCS system, which also improves the utility of participating individuals.