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This paper addresses the challenge of online worker recruitment in mobile crowdsensing (MC) under budget constraints, where worker performance is dynamic rather than stationary. By formulating the problem as a structured bandit model, the authors develop a cost-aware online learning framework that adapts to evolving worker performance and heterogeneous costs, maximizing long-term sensing utility. Experimental results show that their approach significantly outperforms traditional methods that overlook the impact of experience on worker performance or assume fixed costs.
Ignoring the evolving nature of worker performance can lead to suboptimal budget allocation, but this new framework adapts to maximize sensing utility in real-time.
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.
