Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Nan Zhou, Zuxin Li, Fanhang Man, Xuecheng Chen, Susu Xu, Fan Dang, Chaopeng Hong, Yunhao Liu, Xiao-Ping Zhang, Xinlei Chen

TL;DR

This work tackles Quality of Information in non-dedicated vehicular mobile crowdsensing by jointly optimizing sensing coverage and reliability under budget constraints. It introduces Aggregated Sensing Quality (ASQ) and a Mutually Assisted Belief-aware Vehicle Dispatching framework that infers sensor reliability and calculates incentives to guide dispatch decisions. Real-world taxi data and large-scale simulations show significant improvements in ASQ and downstream map reconstruction accuracy, demonstrating the method's effectiveness and scalability for smart-city monitoring. The approach offers cost-efficient, scalable urban sensing without dedicated infrastructure, with broad applicability to traffic, environmental, and other city-scale sensing tasks.

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.

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

TL;DR

This work tackles Quality of Information in non-dedicated vehicular mobile crowdsensing by jointly optimizing sensing coverage and reliability under budget constraints. It introduces Aggregated Sensing Quality (ASQ) and a Mutually Assisted Belief-aware Vehicle Dispatching framework that infers sensor reliability and calculates incentives to guide dispatch decisions. Real-world taxi data and large-scale simulations show significant improvements in ASQ and downstream map reconstruction accuracy, demonstrating the method's effectiveness and scalability for smart-city monitoring. The approach offers cost-efficient, scalable urban sensing without dedicated infrastructure, with broad applicability to traffic, environmental, and other city-scale sensing tasks.

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.

Paper Structure

This paper contains 30 sections, 17 equations, 8 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System model & Definition
  3. Motivation Case Studies
  4. System Models
  5. Problem Formulation
  6. Algorithm Design
  7. Online Sensing Reliability Inferring
  8. Monetary Incentive Calculating
  9. Mutually Assisted Belief-aware Vehicle Dispatching
  10. Time Complexity Analysis
  11. Evaluation
  12. Experiment Setup
  13. Real-World Data Collection and Processing
  14. Simulation Environment Configuration
  15. Virtual Taxi Fleet Modeling
  16. ...and 15 more sections

Figures (8)

  • Figure 1: Motivations for QUIDS.
  • Figure 2: This figure illustrates how dispatching non-dedicated vehicular Mobile Crowdsensing (NVMCS) in uneven sensing reliability setups can improve Quality of Information (QoI) by making the coverage optimal.
  • Figure 3: This figure shows our proposed algorithm's framework.
  • Figure 4: The sensor platform deployed in our taxi, features a GPS receiver, a gas prompt, and four slacks capable of sensing various physical factors across the city.
  • Figure 5: The performance of QUIDS under different factors.
  • ...and 3 more figures