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

TL;DR

This paper introduces PACMAB, a decentralized learning framework for multi-platform mobile crowdsensing that effectively manages incomplete information and competition among platforms, significantly improving task completion rates.

Contribution

It models the multi-platform crowdsensing as a dynamic hypergame and proposes a novel decentralized learning approach to optimize task proposals and acceptances under incomplete information.

Findings

PACMAB outperforms benchmarks by completing at least 41% more tasks.

The framework scales favorably for both MCSPs and MUs.

Extensive simulations validate the effectiveness of PACMAB.

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.