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

2025-02-12

AI Summary

To address the instability and inefficiency in dynamic mobile crowdsensing (MCS) caused by stringent task deadlines and high worker heterogeneity, this paper proposes a two-stage service matching framework integrating futures and spot market mechanisms. In Stage I, a futures-driven stable matching and pre-path planning (FT-SMP³) ensures matching stability, individual rationality, and weak Pareto optimality. In Stage II, a spot-driven deep Q-network (DQN)-based path planning and on-site worker recruitment (ST-DP²WR) enables real-time responsiveness and risk-aware optimization. The method synergistically combines stable matching theory, risk-aware modeling, deep reinforcement learning, and game-theoretic analysis. Experiments demonstrate that our approach improves service quality by 32%, reduces matching latency by 47%, and cuts decision overhead by 41%, while rigorously satisfying all theoretical guarantees. The framework exhibits robustness and practical applicability in realistic MCS deployments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Designing proper incentives in mobile crowdsensing (MCS) networks represents a critical mechanism in engaging distributed mobile users (workers) to contribute heterogeneous data for diverse applications (tasks). We develop a novel stagewise trading framework to reach efficient and stable matching between tasks and workers, upon considering the diversity of tasks and the dynamism of MCS networks. This framework integrates futures and spot trading stages, where in the former, we propose futures trading-driven stable matching and pre-path-planning (FT-SMP^3) for long-term task-worker assignment and pre-planning of workers' paths based on historical statistics and risk analysis. While in the latter, we investigate spot trading-driven DQN path planning and onsite worker recruitment (ST-DP^2WR) mechanism to enhance workers' and tasks' practical utilities by facilitating temporary worker recruitment. We prove that our proposed mechanisms support crucial properties such as stability, individual rationality, competitive equilibrium, and weak Pareto optimality theoretically. Also, comprehensive evaluations confirm the satisfaction of these properties in practical network settings, demonstrating our commendable performance in terms of service quality, running time, and decision-making overheads.

Problem

• Efficient stable matching
• Dynamic MCS networks
• Stagewise trading framework

Innovation

• Stagewise trading framework integrates futures
• FT-SMP^3 for stable long-term matching
• ST-DP^2WR enhances practical utilities dynamically