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New bandit framework optimizes crowdsensing worker recruitment

By PulseAugur Editorial · [3 sources] ·

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.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New bandit framework optimizes crowdsensing worker recruitment

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The cluster contains a research paper published on arXiv detailing a new algorithmic framework.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Yin Huang, Qingsong Liu, Jie Xu ·

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

    arXiv:2607.13546v1 Announce Type: new Abstract: 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 recrui…

  2. arXiv cs.LG TIER_1 English(EN) · Jie Xu ·

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

    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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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…

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