面向事件驱动状态更新系统的基于阈值的脉冲神经网络
Threshold-Based Spiking Neural Networks for Event-Driven Status Update Systems
AI总结：
针对事件驱动状态更新系统中信息新鲜度与能耗的联合优化难题，提出基于脉冲神经网络的轻量级强化学习方法，可可靠学习最优阈值策略，实现更节能的传输决策。
AI中文摘要：
事件驱动传感通过仅在相关事件发生时激活通信，为物联网（IoT）设备提供节能支持。在这类系统中，传输决策由被监控的过程而非预定义的调度决定。因此，由于传输决策受限于随机发生的事件，同时优化信息新鲜度与能耗极具挑战性。为应对这一挑战，本文研究了一种事件驱动状态更新系统，其中唤醒事件遵循被监控过程的动态特性。是否传输传感数据的问题被建模为马尔可夫决策过程（MDP），该过程旨在同时最小化信息年龄（Age of Information, AoI）与传输能耗。我们证明了最优阈值策略的存在性，从而获得了最优传输策略的可解释表征。受此结果启发，我们提出了一种基于脉冲神经网络（Spiking Neural Networks, SNN）的轻量级强化学习（RL）方法，其架构明确表征阈值策略。所得策略表征的复杂度相对于最大AoI为常数，且能实现比等效人工神经网络（Artificial Neural Network, ANN）更节能的实现。数值结果表明，所提出的SNN可在不同工作场景下可靠学习最优阈值。
英文摘要：
Event-driven sensing supports energy-efficient Internet-of-Things (IoT) devices by activating communication only when relevant events occur. In such systems, transmission decisions are governed by the monitored process rather than predefined schedules. Consequently, jointly optimising information freshness and energy consumption is challenging because transmission decisions are restricted to randomly occurring events. To address this challenge, we investigate an event-driven status update system in which wake-up events follow the dynamics of the monitored process. The problem of determining whether to transmit the sensing data or not is cast as a Markov Decision Process (MDP) that jointly minimises the Age of Information (AoI) and transmission energy. We prove the existence of an optimal threshold policy, thereby obtaining an interpretable characterisation of the optimal transmission strategy. Motivated by this result, we propose a lightweight Reinforcement Learning (RL) approach based on Spiking Neural Networks (SNNs) whose architecture explicitly represents threshold policies. The resulting policy representation has constant complexity with respect to the maximum AoI and enables a more energy-efficient implementation than a comparable Artificial Neural Network (ANN). Numerical results demonstrate that the proposed SNN reliably learns optimal thresholds across different operating regimes.
