Reputation-Based Reverse Combination Auction Incentive Method to Encourage Vehicles to Participate in the VCS System

Abstract

Vehicular crowdsensing (VCS) brings more possibilities for data collection and event detection of road networks with low cost and high mobility of vehicles. However, due to resource constraints and other reasons, not all vehicles are willing to participate in the VCS system. In this paper, we aim to propose an effective and fair incentive method in a VCS system that considers the benefits of both the platform and participants. To solve the problem of insufficient participants and low willingness to participate, we propose a reputation-based reverse combination auction (RBRCA) incentive method that includes an approximate RBRCA-VPS algorithm that selects winners to maximize the utility of the platform and a payment determination algorithm based on both monetary and virtual currency rewards. Moreover, we consider complex real scenarios. To ensure the accuracy of the long-term stability and analysis of the VCS system, we include the reputation of vehicles, which consists of the quality of sensing data (QoD) and the participation ratio. Additionally, we consider the task priority and budget, and RBRCA can complete high-priority tasks first with a limited budget. Simulation results show that RBRCA has good performance in motivating vehicles to participate, controlling the budget and choosing winners.