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.

翻译：微型机器学习（TinyML）的进步推动了智能农业、医疗健康和智慧城市等智能工业解决方案的发展。尽管相关研究致力于在受限硬件上实现TinyML解决方案，但通过优化电池供电系统的能量消耗来增强实际应用的需求日益凸显。本文通过优化基于电池供电的图像异常检测物联网（IoT）系统，扩展并推动了TinyML研究。虽然该领域先前的工作已实现设备端推理与训练能力，但尚未有研究探讨利用强化学习（RL）等机器学习方法优化此类能力的管理，以延长系统的部署电池寿命。通过建模仿真，本文将强化学习算法的电池寿命影响与静态和动态优化方法进行基准测试，并为后续硬件基准测试奠定基础。研究表明，在支持TinyML的物联网系统中使用强化学习优化系统操作（包括云端异常处理与设备端训练），相比静态和动态优化方法，电池寿命分别提升22.86%和10.86%。所提出的解决方案内存占用仅800B（且可进一步缩减），因此可部署于资源受限硬件。这进一步推动了此类系统在智能农业等关键领域的实际部署。