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Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

Houyi Qi, Minghui Liwang, Xianbin Wang, Liqun Fu, Yiguang Hong, Li Li, Zhipeng Cheng

TL;DR

This work tackles stable task-worker matching in dynamic mobile crowdsensing by introducing a stagewise trading framework that interleaves futures-based long-term contracts with spot-based real-time adjustments. The FT-SMP$^3$ component performs risk-aware, stable M2M matching and pre-path planning via an enhanced ant colony approach and a DP-based optimization, while ST-DP$^2$WR provides a fast backup via DQNP-DRO for online routing and temporary recruitment. The framework is shown to satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality, with extensive simulations and real-world data demonstrating superior service quality, social welfare, and reduced decision-making overhead compared with baselines. The results indicate practical impact for robust, scalable service trading in dynamic MCS settings and point to future directions around smart contracts and cross-domain collaboration.

Abstract

Designing effective incentive mechanisms in mobile crowdsensing (MCS) networks is crucial for engaging distributed mobile users (workers) to contribute heterogeneous data for various applications (tasks). In this paper, we propose a novel stagewise trading framework to achieve efficient and stable task-worker matching, explicitly accounting for task diversity (e.g., spatio-temporal limitations) and network dynamics inherent in MCS environments. This framework integrates both futures and spot trading stages. In the former, we introduce the \textbf{f}utures \textbf{t}rading-driven \textbf{s}table \textbf{m}atching and \textbf{p}re-\textbf{p}ath-\textbf{p}lanning mechanism (FT-SMP$^3$), which enables long-term task-worker assignment and pre-planning of workers' trajectories based on historical statistics and risk-aware analysis. In the latter, we develop the \textbf{s}pot \textbf{t}rading-driven \textbf{D}QN-based \textbf{p}ath \textbf{p}lanning and onsite \textbf{w}orker \textbf{r}ecruitment mechanism (ST-DP$^2$WR), which dynamically improves the practical utilities of tasks and workers by supporting real-time recruitment and path adjustment. We rigorously prove that the proposed mechanisms satisfy key economic and algorithmic properties, including stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Extensive experiements further validate the effectiveness of our framework in realistic network settings, demonstrating superior performance in terms of service quality, computational efficiency, and decision-making overhead.

Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

TL;DR

This work tackles stable task-worker matching in dynamic mobile crowdsensing by introducing a stagewise trading framework that interleaves futures-based long-term contracts with spot-based real-time adjustments. The FT-SMP component performs risk-aware, stable M2M matching and pre-path planning via an enhanced ant colony approach and a DP-based optimization, while ST-DPWR provides a fast backup via DQNP-DRO for online routing and temporary recruitment. The framework is shown to satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality, with extensive simulations and real-world data demonstrating superior service quality, social welfare, and reduced decision-making overhead compared with baselines. The results indicate practical impact for robust, scalable service trading in dynamic MCS settings and point to future directions around smart contracts and cross-domain collaboration.

Abstract

Designing effective incentive mechanisms in mobile crowdsensing (MCS) networks is crucial for engaging distributed mobile users (workers) to contribute heterogeneous data for various applications (tasks). In this paper, we propose a novel stagewise trading framework to achieve efficient and stable task-worker matching, explicitly accounting for task diversity (e.g., spatio-temporal limitations) and network dynamics inherent in MCS environments. This framework integrates both futures and spot trading stages. In the former, we introduce the \textbf{f}utures \textbf{t}rading-driven \textbf{s}table \textbf{m}atching and \textbf{p}re-\textbf{p}ath-\textbf{p}lanning mechanism (FT-SMP), which enables long-term task-worker assignment and pre-planning of workers' trajectories based on historical statistics and risk-aware analysis. In the latter, we develop the \textbf{s}pot \textbf{t}rading-driven \textbf{D}QN-based \textbf{p}ath \textbf{p}lanning and onsite \textbf{w}orker \textbf{r}ecruitment mechanism (ST-DPWR), which dynamically improves the practical utilities of tasks and workers by supporting real-time recruitment and path adjustment. We rigorously prove that the proposed mechanisms satisfy key economic and algorithmic properties, including stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Extensive experiements further validate the effectiveness of our framework in realistic network settings, demonstrating superior performance in terms of service quality, computational efficiency, and decision-making overhead.

Paper Structure

This paper contains 44 sections, 14 theorems, 62 equations, 8 figures, 5 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Overview and System Model
  4. Overview
  5. Basic Modeling
  6. Proposed FT-SMP$^3$
  7. Key Modeling
  8. Utility, expected utility, and risk of workers
  9. Utility, expected utility, and risk of tasks
  10. Key Definitions of Matching
  11. Problem Formulation
  12. Solution Design
  13. Proposed EACO-P$^3$TR Algorithm
  14. Proposed FT-M2M matching algorithm
  15. Proposed ST-DP$^2$WR Mechanism
  16. ...and 29 more sections

Key Result

Lemma 1

(Convergence of FT-M2M matching) Alg. 2 converges within finite rounds.

Figures (8)

  • Figure 1: Framework and procedure in terms of a timeline associated with our proposed the stagewise trading framework in dynamic MCS networks.
  • Figure 2: A flow chart, showing the distributed task-worker interaction model regarding our proposed FT-SMP$^3$.
  • Figure 3: A detailed structure of the ST-DP$^2$WR.
  • Figure 4: Performance comparisons in terms of overall utility, overall service quality and social welfare under different problem sizes, where (a)-(d) consider 15 workers, and (e)-(h) consider 30 workers in the network.
  • Figure 5: Performance comparisons in terms of PoDSQ and PoTAW.
  • ...and 3 more figures

Theorems & Definitions (44)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Definition 7
  • Definition 8
  • Lemma 1
  • proof
  • ...and 34 more