发表机构
University of Florida; University of Massachusetts Amherst（佛罗里达大学; 马萨诸塞大学阿默斯特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究众包感知中预算受限的在线工人招募问题，工人绩效随经验变化，成本未知。提出成本感知在线学习框架，建模为结构化策略模型，能联合学习奖励轨迹与成本，检测性能饱和，分配预算最大化感知效用，实验验证优于基线方法。
AI 中文摘要
移动众包感知（MC）招募移动用户使用智能手机执行感知任务，实现交通监测和环境感知等大规模应用。一个基本挑战是在不确定性下进行在线工人招募，平台在预算有限的情况下必须了解工人的感知性能。现有基于学习的MC招募方法通常假设每个工人的感知质量随时间固定不变。然而，实际中工人绩效常随经验提升并最终稳定，且由于时变设备和上下文状态，产生的感知成本可能事先未知。本文研究预算受限的在线招募问题，平台在每轮选择一名工人，观察感知质量和成本，每个工人的预期感知质量随经验增加并最终趋于平稳，重复此过程直到预算耗尽。我们将此问题建模为结构化策略模型，开发了一个成本感知在线学习框架，联合学习不断变化的奖励轨迹和异构成本，检测性能饱和，并分配有限预算以最大化长期感知效用。我们提供了理论性能保证，并通过大量实验验证了所提方法，表明其相对于忽略经验驱动动态或假设已知成本的基线方法有持续改进。
英文摘要
Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing. A fundamental challenge is online worker recruitment under uncertainty, where the platform must learn workers' sensing performance while operating with a limited budget. Existing learning-based MC recruitment methods typically assume that each worker's sensing quality is stationary with a fixed mean over time. In practice, however, worker performance often improves with experience and eventually stabilizes, while the incurred sensing cost can be unknown in advance due to time-varying device and context states. In this paper, we study a budget-constrained online recruitment problem in which the platform selects one worker in each round, observes the sensing quality and incurred cost, where the expected sensing quality of each worker increases with experience and eventually converges to a plateau, and repeats until the budget is exhausted. We formulate this problem as a structured bandit model where each worker's expected reward evolves according to an unknown increasing-then-converging function of its participation count, and each worker has an unknown expected cost. We develop a cost-aware online learning framework that jointly learns evolving reward trajectories and heterogeneous costs, detects performance saturation, and allocates the limited budget to maximize long-term sensing utility. We provide theoretical performance guarantees and validate the proposed approach through extensive experiments, demonstrating consistent improvements over baselines that ignore experience-driven dynamics or assume known costs.
Comments14 pages, 10 figures, 3 tables, submitted for possible journal publication
