Researchers have developed a new cost-aware bandit framework to optimize worker recruitment in mobile crowdsensing. This framework addresses the challenge of evolving worker performance, where individuals improve with experience, and accounts for unknown, variable costs. The model jointly learns worker performance trajectories and costs, identifies when performance saturates, and allocates a limited budget to maximize long-term sensing utility. Experiments show consistent improvements over methods that ignore these dynamics or assume fixed costs. AI
IMPACT This framework could improve the efficiency and cost-effectiveness of large-scale data collection in mobile crowdsensing applications.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new algorithmic framework.
- alphaXiv
- arXiv
- Bandits
- CatalyzeX
- Cost-aware bandits
- crowdsensing
- cs.LG
- DagsHub
- Gotit.pub
- Hugging Face
- IArxiv Recommender
- Influence Flower
- Online Learning
- ScienceCast
- Structured bandit model
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