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.

翻译：移动群智感知（MCS）是未来无线网络中一种极具前景的分布式感知范式，其中移动群智感知平台（MCSPs）通过货币激励招募移动单元（MUs）进行传感数据采集。尽管现有研究大多假设单个MCSP存在，但在实际部署中涉及多个相互竞争的MCSPs，它们同时向MUs提出任务报价，而MUs则选择能最大化自身收益的报价。这种交互形成了基于合同的双方匹配博弈（MWC），可分解为两个组成部分：（i）MCSPs的任务提议问题与（ii）MUs的任务接受问题。为最优求解（i），每个MCSP需预先掌握其他平台的偏好及MUs的质量信息；类似地，为最优求解（ii），MUs需预先知晓所有任务的执行工作量信息。然而MCSPs与MUs均无法获取此类信息。针对其他MCSP偏好未知的挑战，将MWC建模为动态超博弈，其中每个MCSP通过感知机制建模未知偏好，并在重复交互中逐步优化认知。为求解不完全信息下的动态超博弈，本文提出PACMAB——一种完全去中心化的感知感知双方学习框架，其中（i）每个MCSP在竞争环境下学习自适应任务提议策略，（ii）每个MU通过预估任务执行工作量学习任务接受策略。PACMAB的计算复杂度分析表明其在MCSPs与MUs两端均具有良好的可扩展性。大量仿真实验证明，PACMAB在不依赖完全信息的前提下，始终能够超越基准方法，至少多完成41%的任务。