QUIDS: Quality-informed Incentive-driven Multi-agent Dispatching System for Mobile Crowdsensing

TL;DR

QUIDS is a novel multi-agent dispatching system that enhances the quality of information in vehicular crowdsensing by integrating coverage, reliability, and incentives, leading to significant improvements in sensing quality and accuracy.

Contribution

It introduces a new metric, ASQ, and a belief-aware dispatching algorithm to optimize sensing quality under budget constraints in urban crowdsensing.

Findings

ASQ improves by 38% over non-dispatching scenarios

Reduces map reconstruction errors by 39-74%

Enhances sensing reliability and coverage efficiently

Abstract

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.