Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning

TL;DR

This paper introduces a CMM-based approach for runtime adaptation in wireless sensor nodes, demonstrating it outperforms Q-Learning in adaptation performance while maintaining similar resource use.

Contribution

It presents a novel CMM-based method for self-aware sensor nodes and compares it to Q-Learning, highlighting advantages in adaptation performance.

Findings

CMM methods outperform Q-Learning in adaptation performance

CMM approaches require similar resources as RL methods

Simulation and prototype validate CMM effectiveness

Abstract

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.