Dynamic Hypergame for Task Assignment in Multi-platform Mobile Crowdsensing Under Incomplete Information

2026-05-05

AI Summary

This study addresses the challenge of task allocation and user selection in multi-platform mobile crowdsensing under incomplete information by introducing dynamic hypergame theory into this domain for the first time. The authors propose PACMAB, a fully decentralized learning framework that integrates sensing-aware modeling, adaptive reinforcement learning, and a bilateral matching mechanism. Through perception-driven collaborative learning, PACMAB simultaneously optimizes platform task assignment and user task acceptance strategies without requiring global information. Experimental results demonstrate that PACMAB improves task completion rates by at least 41% compared to baseline methods while maintaining strong scalability and computational efficiency.

Abstract

Mobile crowdsensing (MCS) is a promising distributed sensing paradigm for future wireless networks, where MCS platforms (MCSPs) recruit mobile units (MUs) through monetary incentives for sensing data collection. While most existing studies assume a single MCSP, practical deployments involve multiple competing MCSPs that simultaneously propose task offers to MUs, and MUs accept offers that maximize their revenue. This interaction gives rise to a two-sided matching game with contracts (MWC), decomposed into two components: (i) task proposal problem of the MCSPs and (ii) task acceptance problem of the MUs. To optimally solve (i), every MCSP requires information about other platforms' preferences and the qualities of the MUs in advance. Similarly, to solve (ii) optimally, the MUs require information about the task execution efforts of all tasks in advance. Such information is unavailable at the MCSPs and at the MUs. To address the challenge of unknown preferences of the other MCSPs, the MWC is posed as a dynamic hypergame, where every MCSP models the unknown preferences through perceptions and refines them over repeated interactions. To solve the dynamic hypergame under incomplete information, we propose PACMAB, a fully decentralized perception-aware two-sided learning framework where, (i) each MCSP learns an adaptive task proposal strategy under competition, and (ii) each MU learns task acceptance policy by estimating task execution efforts. Computational complexity of PACMAB shows that it scales favorably for the MCSPs as well as the MUs. Extensive simulations show that PACMAB consistently outperforms the benchmarks by completing at least 41% more tasks without assuming complete information.

Problem

• Mobile Crowdsensing
• Incomplete Information
• Task Assignment
• Multi-platform Competition
• Two-sided Matching

Innovation

• dynamic hypergame
• incomplete information
• mobile crowdsensing
• decentralized learning
• two-sided matching