Constructing an accurate representation model of phenomena with fewer measurements is a fundamental challenge in the Internet of Things. Leveraging sparse sensing policies to select the most informative measurements is a prominent technique for addressing resource constraints. However, designing such sensing policies requires significant domain knowledge and involves manually fine-tuned heuristics that are task-specific and often non-adaptive. In this work, we propose reducing manual-engineering efforts in designing sensing policies by using an automated approach based on deep reinforcement learning. Guided by an uncertainty-aware prediction model, the sensors learn sensing behaviors autonomously by optimizing an application goal formulated in the reward function based on the measured peaks-over-threshold. We apply the proposed approach in two use cases of monitoring air quality and indoor noise and show the adaptability and transferability of the learned policies. Compared to conventional periodic sensing methods, our results achieve, on average, an increased detection in periods of interest by 78.5% and 357.3% while reducing energy expenditure by 14.3% and 7.6% for air quality and noise monitoring, respectively. Additionally, the resulting representation models are more credible, as measured by various metrics of probabilistic modeling.

中文翻译：

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具有深度强化学习的不确定性感知自主传感

用更少的测量构建现象的准确表示模型是物联网中的一个基本挑战。利用稀疏传感策略来选择信息最丰富的测量是解决资源限制的一项重要技术。然而，设计此类传感策略需要大量的领域知识，并涉及手动微调的启发式方法，这些启发式方法是特定于任务的且通常是非自适应的。在这项工作中，我们建议通过使用基于深度强化学习的自动化方法来减少设计传感策略时的手动工程工作。在不确定性感知预测模型的指导下，传感器根据测量的阈值峰值优化奖励函数中制定的应用目标，从而自主学习传感行为。我们将所提出的方法应用于监测空气质量和室内噪声的两个用例，并展示了所学策略的适应性和可移植性。与传统的周期性传感方法相比，我们的结果平均将感兴趣时段的检测量增加了 78.5% 和 357.3%，同时空气质量和噪声监测的能源消耗分别减少了 14.3% 和 7.6%。此外，根据概率建模的各种指标来衡量，所得的表示模型更加可信。