SHIP: A State-Aware Hybrid Incentive Program for Urban Crowd Sensing With for-Hire Vehicles
Han Jiang, Yilong Ren, Jing Fang, Yang Yang, Liang Xu, Haiyang Yu
Abstract
Benefiting from unified sensors and long-term traffic engagement, for-hire vehicles (FHVs) are widely considered the mainstay for vehicular crowd sensing (VCS) tasks. However, incentivizing FHVs to participate in sensing tasks remains a fundamental challenge for FHV-enabled VCS systems: for one thing, the distribution diversity of orders and tasks limits FHVs from participating in VCS; for another, FHVs’ operating states determine whether they are free to execute sensing tasks. To address the above issues, this article proposes SHIP, a State-aware Hybrid Incentive Program for FHV-enabled VCS systems. Our proposal finely categorizes FHVs’ operating states and provides a hybrid incentive scheme that incorporates both opportunistic and participatory approaches. We also introduce coverage diversity to reflect the distribution of FHVs and sensing tasks. By combining coverage diversity with vehicle revenue, we establish a dynamic multi-objective optimization model to select appropriate FHVs for sensing to achieve a multi-win situation. Experiments based on real-world datasets show that our proposal can effectively utilize FHVs with different operating states to improve the quality of sensing tasks while increasing FHVs’ revenue.