为能量采集传感器设计最优数据删减策略，提升传输能效。
An MDP Model for Censoring in Harvesting Sensors: Optimal and Approximated Solutions
- 将删减问题建模为无限时域马尔可夫决策过程，优化消息重要性总和。
- 在特定电池模型下，最优策略为基于重要性的阈值机制，仅传高于阈值的消息。
- 提出低复杂度近似算法，收敛更快，适用于单跳与多跳网络。
本文为能量采集传感器提出一种新型数据删减策略，以实现节能传输。该问题被建模为无限时域马尔可夫决策过程（MDP），目标是最小化所有传输消息的预期重要性总和。假设重要性可在发送节点评估，我们证明，在特定电池模型条件下，最优删减策略是重要性值的阈值函数：仅当消息重要性超过与当前电池水平相关的阈值时才传输。利用此性质，我们提出一种基于模型的随机近似方案，相比传统Q-learning算法，计算复杂度更低且收敛速度更快。单跳与多跳网络的数值实验验证了所提方案的理论优势。
原文摘要 · Abstract (English)
In this paper, we propose a novel censoring policy for energy-efficient transmissions in energy-harvesting sensors. The problem is formulated as an infinite-horizon Markov Decision Process (MDP). The objective to be optimized is the expected sum of the importance (utility) of all transmitted messages. Assuming that such importance can be evaluated at the transmitting node, we show that, under certain conditions on the battery model, the optimal censoring policy is a threshold function on the importance value. Specifically, messages are transmitted only if their importance is above a threshold whose value depends on the battery level. Exploiting this property, we propose a model-based stochastic scheme that approximates the optimal solution, with less computational complexity and faster convergence speed than a conventional Q-learning algorithm. Numerical experiments in single-hop and multi-hop networks confirm the analytical advantages of the proposed scheme.
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