# From Novice to Expert: Cost-Aware Bandits for Evolving Worker Performance in Crowdsensing

arXiv: https://arxiv.org/abs/2607.13546 · HTML: https://mlsanity.com/paper/2607.13546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Weighted Linear Bandits for Non-Stationary Environments](https://mlsanity.com/paper/1909.09146/) (2019)
- [Bandits with concave rewards and convex knapsacks](https://mlsanity.com/paper/1402.5758/) (2014)
- [Adversarial Bandits with Knapsacks](https://mlsanity.com/paper/1811.11881/) (2018)
- [Rotting Bandits](https://mlsanity.com/paper/1702.07274/) (2017)
- [Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit](https://mlsanity.com/paper/1802.03692/) (2018)
- [An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives](https://mlsanity.com/paper/1506.03374/) (2015)
- [Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits](https://mlsanity.com/paper/2403.07213/) (2024)
- [Non-stationary Bandits with Knapsacks](https://mlsanity.com/paper/2205.12427/) (2022)
- [Stochastic Rising Bandits](https://mlsanity.com/paper/2212.03798/) (2022)
- [The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks](https://mlsanity.com/paper/2102.06385/) (2021)
- [Unifying the stochastic and the adversarial Bandits with Knapsack](https://mlsanity.com/paper/1811.12253/) (2018)
- [Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals](https://mlsanity.com/paper/2306.07071/) (2023)
- [Learning the Optimal Path and DNN Partition for Collaborative Edge Inference](https://mlsanity.com/paper/2410.01857/) (2024)
- [A single algorithm for both restless and rested rotting bandits](https://mlsanity.com/paper/2604.21432/) (2026)

## Abstract (arXiv)

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.