QUIDS: Quality-Informed Incentive-Driven Multiagent Dispatching System for Mobile Crowdsensing

This article addresses the challenges of achieving optimal quality of information (QoI) in a nondedicated vehicular mobile crowdsensing (NVMCS) system, where vehicles not originally designed for sensing are leveraged to collect real-time data as they traverse urban environments. These challenges are exacerbated by the interrelated issues of sensing coverage, sensing reliability, and the inherently dynamic nature of participating vehicles. To tackle these challenges, we propose QUIDS, a quality-informed incentive-driven multiagent dispatching system, which ensures high sensing coverage and sensing reliability under budget constraints in NVMCS systems. QUIDS improves QoI by introducing a novel metric, aggregated sensing quality (ASQ), designed to quantitatively capture the concept of QoI by integrating both sensing coverage and sensing reliability. Moreover, we develop a mutually assisted belief-aware vehicle dispatching algorithm that estimates sensing reliability and allocates monetary incentives under uncertain vehicle conditions, thereby further improving ASQ. Evaluation using real-world data collected from a deployed NVMCS system in a metropolitan area demonstrates the effectiveness of QUIDS. The ASQ metric shows a 38% improvement over nondispatching scenarios and a 10% enhancement over state-of-the-art methods. In addition, QUIDS reduces reconstruction map errors by 39%–74% across various reconstruction algorithms, validating its efficacy in improving QoI within NVMCS systems. Addressing the often-overlooked issue of sensing reliability in existing studies, the QUIDS system leverages nondedicated vehicles and incorporates a quality-informed, incentive-driven dispatching system to jointly optimize sensing coverage and sensing reliability. This enables low-cost, high-quality, and scalable urban environmental monitoring without the need for dedicated sensing infrastructure, and makes the system applicable to diverse smart-city scenarios such as traffic monitoring and environmental sensing.