tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices
tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices
复制标题
DOI：
--
复制
发表时间：
2022-02
期刊：
影响因子：
--
通讯作者：
T. Basaklar;Y. Tuncel;U. Ogras
中科院分区：
文献类型：
--
作者：
T. Basaklar;Y. Tuncel;U. Ogras
低功耗电子和机器学习技术的进步导致了许多新颖的可穿戴物联网设备。这些设备的电池容量和计算能力有限。因此，从环境源收集能量是为这些低能量可穿戴设备供电的有前途的解决方案。他们需要最佳地管理收集的能量，以实现能源中性操作，从而消除充电要求。由于目标设备的采集能量的动态性质和电池能量约束，最佳能量管理是一项具有挑战性的任务。为了应对这一挑战，我们提出了一个基于强化学习的能源管理框架tinyMAN，用于资源受限的可穿戴物联网设备。该框架在动态能量收集模式和电池约束下最大化目标设备的利用率。此外，tinyMAN不依赖于对收获能量的预测，这使得它成为一种无预测的方法。我们使用TensorFlow Lite for Micro在可穿戴设备原型上部署了tinyMAN，这要归功于其不到100 KB的小内存占用。我们的评估表明，tinyMAN实现不到2.36毫秒和27.75 $\mu$J，同时保持高达45%的实用性相比，以前的方法。
Advances in low-power electronics and machine learning techniques lead to many novel wearable IoT devices. These devices have limited battery capacity and computational power. Thus, energy harvesting from ambient sources is a promising solution to power these low-energy wearable devices. They need to manage the harvested energy optimally to achieve energy-neutral operation, which eliminates recharging requirements. Optimal energy management is a challenging task due to the dynamic nature of the harvested energy and the battery energy constraints of the target device. To address this challenge, we present a reinforcement learning-based energy management framework, tinyMAN, for resource-constrained wearable IoT devices. The framework maximizes the utilization of the target device under dynamic energy harvesting patterns and battery constraints. Moreover, tinyMAN does not rely on forecasts of the harvested energy which makes it a prediction-free approach. We deployed tinyMAN on a wearable device prototype using TensorFlow Lite for Micro thanks to its small memory footprint of less than 100 KB. Our evaluations show that tinyMAN achieves less than 2.36 ms and 27.75 $\mu$J while maintaining up to 45% higher utility compared to prior approaches.