Online participant selection incorporating coverage quality and participant ability for edge-aided vehicular crowdsensing

Abstract

Recently, the edge-aided vehicular crowdsensing (EAVC) system has become a promising data collection mode, which utilizes vehicles to collect sensing data under the guidance of edge servers. Participant selection is a fundamental problem in vehicular crowdsensing. The available relevant schemes are unsuitable for newly arrived participants, ignore the differentiated sensing requirements of different areas, and underestimate heterogeneity among participants, which seriously damages the service quality. To handle these problems, this paper proposes an improved reinforcement learning-based online participant selection scheme incorporating coverage quality and participant ability (PSCQA) in EAVC. Coverage quality considering different spatiotemporal partitions is formulated based on the entropy theory to measure coverage uniformity of all areas and the coverage degree of the hotspot areas. Participant ability is designed by combining data quality, movement predictability, and priorities of passing areas to comprehensively measure performance differences among participants. In PSCQA, the coverage quality and ability of the selected participants are optimized through two separate value functions. In particular, participants are dynamically grouped into vehicle clusters based on the similarity of their trajectories to solve the state explosion problem that plagues traditional Q-learning. Simulation results on a real-world dataset demonstrate that the proposed PSCQA outperforms other reinforcement learning-based online participant selection schemes and traditional offline participant selection schemes.