Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy

Abstract

Operational cycle control is an attractive field of research which can lead to improvements in the services offered by power-aware monitoring embedded IoT devices. Machine learning (ML) is an infrastructure for operational cycle control and provides many approaches which provide more energyefficient operation. One subfield of ML is Q-learning (QL), which forms the basis of the data-driven self-learning (DDSL) controller. The DDSL algorithm dynamically sets operational duty cycles according to estimates of future collected data values, leading to effective operation of power-aware systems. However, QL performs very poorly in stochastic environments as a result of overestimation of action values. The double estimator implemented in QL therefore applies Double QL (DQL) and forms the basis for a novel Double DDSL (DDDSL). The results of testing a DDDSL controller on historical data showed 42-50 % greater performance than a controller with a fixed duty-cycle, and 2-12 % more performance than a DDSL controller.

A Power-Aware Method for IoT Networks with Mobile Stations and Dynamic Power Management Strategy

“…The results showed that these strategies reduced power consumption and improved energy efficiency. In [17], a data-driven self-learning controller model method was presented for power-aware IoT nodes, based on a double Qlearning strategy. This method used machine learning to improve power consumption in IoT nodes based on historical data.…”