论文标题
使用结构化学习中无线传感器节点的运行时适应
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning
论文作者
论文摘要
马尔可夫决策过程（MDP）提供了促进运行时网络物理系统的动态适应和自我优化的重要功能。近年来，这主要采用了增强学习（RL）技术的形式，这些技术消除了一些MDP组件，以减少计算要求。在这项工作中，我们表明，紧凑型MDP模型（CMM）的最新进展为设计无线传感器网络节点时质疑这一趋势提供了足够的理由。在这项工作中，提出了一种基于CMM的新型方法，用于设计自我意识的无线传感器节点，并将其与流行的RL技术Q-Learning进行了比较。我们表明，RL方法不能很好地服务于某些类别的CPS节点，在这种情况下，对比度RL与CMM方法相比。通过仿真和原型实现，我们证明了CMM方法相对于Q学习，可以提供更好的运行时适应性性能，并具有可比的资源要求。
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.
