基于结构化学习的无线传感器节点自适应算法
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning
摘要：
马尔可夫决策过程（MDP）为促进网络物理系统在运行时的动态适应和自优化提供了重要的能力。近年来，这主要采取强化学习（RL）技术的形式，消除了一些MDP组件，以减少计算需求。在这项工作中，我们表明，最近的进展紧凑MDP模型（CMM）提供了足够的理由来质疑这种趋势时，设计无线传感器网络节点。在这项工作中，提出了一种新的基于CMM的方法来设计具有自我意识的无线传感器节点，并与Q-Learning（一种流行的RL技术）进行了比较。我们表明，某种类型的CPS节点是不是很好地服务于RL方法，并对比RL与CMM方法在这种情况下。通过模拟和原型实现，我们证明了CMM方法可以提供更好的运行时自适应性能相对于Q-Learning，具有可比的资源需求。
英文摘要：
Markov Decision Processes (MDPs) provide important capabilities for facilitating the dynamic adaptation and self-optimization of cyber physical systems at runtime. In recent years, this has primarily taken the form of Reinforcement Learning (RL) techniques that eliminate some MDP components for the purpose of reducing computational requirements. In this work, we show that recent advancements in Compact MDP Models (CMMs) provide sufficient cause to question this trend when designing wireless sensor network nodes. In this work, a novel CMM-based approach to designing self-aware wireless sensor nodes is presented and compared to Q-Learning, a popular RL technique. We show that a certain class of CPS nodes is not well served by RL methods, and contrast RL versus CMM methods in this context. Through both simulation and a prototype implementation, we demonstrate that CMM methods can provide significantly better runtime adaptation performance relative to Q-Learning, with comparable resource requirements.
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