  Advances in Tiny Machine Learning (TinyML) have bolstered the creation of
smart industry solutions, including smart agriculture, healthcare and smart
cities. Whilst related research contributes to enabling TinyML solutions on
constrained hardware, there is a need to amplify real-world applications by
optimising energy consumption in battery-powered systems. The work presented
extends and contributes to TinyML research by optimising battery-powered
image-based anomaly detection Internet of Things (IoT) systems. Whilst previous
work in this area has yielded the capabilities of on-device inferencing and
training, there has yet to be an investigation into optimising the management
of such capabilities using machine learning approaches, such as Reinforcement
Learning (RL), to improve the deployment battery life of such systems. Using
modelled simulations, the battery life effects of an RL algorithm are
benchmarked against static and dynamic optimisation approaches, with the
foundation laid for a hardware benchmark to follow. It is shown that using RL
within a TinyML-enabled IoT system to optimise the system operations, including
cloud anomaly processing and on-device training, yields an improved battery
life of 22.86% and 10.86% compared to static and dynamic optimisation
approaches respectively. The proposed solution can be deployed to
resource-constrained hardware, given its low memory footprint of 800 B, which
could be further reduced. This further facilitates the real-world deployment of
such systems, including key sectors such as smart agriculture.
