Abstract
We address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. Real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to a reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.
Fan DANG
Assistant Professor
My research interests include industrial intelligence and edge computing.