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Adaptive Sampling for Continuous Crowdsensing in Unknown Dynamics Environments
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AI
DOI：10.1109/TON.2026.3667004.png)
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Abstract
En 中文
Continuous crowdsensing in Mobile CrowdSensing (MCS) involves ongoing monitoring to gather real-time data over extended periods. A key challenge is determining appropriate intervals between consecutive data samplings to capture temporal variations, especially in unknown dynamic environments. Traditional Age-of-Information (AoI) driven methods maintain data freshness but can be costly and result in data redundancy. To address this, we integrate the AoI metric with information entropy difference to create a novel indicator, Composite Data Value (CDV), balancing data freshness and redundancy. Based on it, we investigate the online adaptive sampling problem for continuous crowdsensing in unknown dynamic environments. This problem is challenging due to the vast space of sensing strategies, difficulty in estimating rewards with unknown distributions and varying rates of change, and the degradation of optimal strategies as the environment evolves. Using a multi-armed bandit framework, we propose $\textsf {AdaScs}$ , an online adaptive sampling optimization approach to maximize long-term CDV performance. First, $\textsf {AdaScs}$ develops compact sensing strategies through limited trials. Then, it adaptively performs online sampling based on evolving reward estimations, identifying optimal strategies, detecting environmental drifts, updating strategies, and adjusting cycle lengths. Our results show that $\textsf {AdaScs}$ outperforms all baselines, with accuracy increasing by 22.6% and reaction time at last improving by 54.6%.
Keywords:
CrowdSensing
sampling frequency
multi-arm bandit
online learning
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