通过激励机制提升车辆众包感知质量，实现低成本高精度城市监测。
QUIDS: Quality-informed Incentive-driven Multi-agent Dispatching System for Mobile Crowdsensing
- 基于感知质量设计激励调度系统，动态优化覆盖与可靠性。
- 真实数据测试显示，感知质量提升38%，地图重建误差降低39%-74%。
- 适合智能交通、环境监测等无专用设备的城市场景应用。
本文针对非专用车载众包感知（NVMCS）系统中难以保障信息质量（QoI）的问题，提出一种质量驱动的多智能体调度系统QUIDS。该系统在预算约束下，通过引入聚合感知质量（ASQ）指标，量化融合感知覆盖与可靠性。设计了互助信念感知的车辆调度算法，在不确定性下估计感知可靠性并分配激励，进一步提升ASQ。基于真实城市部署数据评估显示，QUIDS相比非调度场景使ASQ提升38%，优于现有方法10%；各类算法的地图重建误差降低39%-74%。通过质量导向的激励机制联合优化覆盖与可靠性，实现无需专用基础设施的低成本、高质量城市感知，适用于交通与环境监测等智慧城市场景。
原文摘要 · Abstract (English)
This paper addresses the challenge of achieving optimal Quality of Information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) systems. The key obstacles are the interrelated issues of sensing coverage, sensing reliability, and the dynamic participation of vehicles. To tackle these, we propose QUIDS, a QUality-informed Incentive-driven multi-agent Dispatching System, which ensures high sensing coverage and reliability under budget constraints. QUIDS introduces a novel metric, Aggregated Sensing Quality (ASQ), to quantitatively capture QoI by integrating both coverage and reliability. We also develop a Mutually Assisted Belief-aware Vehicle Dispatching algorithm that estimates sensing reliability and allocates incentives under uncertainty, further improving ASQ. Evaluation using real-world data from a metropolitan NVMCS deployment shows QUIDS improves ASQ by 38% over non-dispatching scenarios and by 10% over state-of-the-art methods. It also reduces reconstruction map errors by 39-74% across algorithms. By jointly optimizing coverage and reliability via a quality-informed incentive mechanism, QUIDS enables low-cost, high-quality urban monitoring without dedicated infrastructure, applicable to smart-city scenarios like traffic and environmental sensing.
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