Adaptive Energy Management for Self-Sustainable Wearables in Mobile Health
Adaptive Energy Management for Self-Sustainable Wearables in Mobile Health
复制标题
移动医疗中自我可持续可穿戴设备的自适应能源管理
DOI：
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
J. Doppa
中科院分区：
文献类型：
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作者：
Dina Hussein;Ganapati Bhat;J. Doppa
集成多个传感器、处理器和通信技术的可穿戴设备有潜力改变移动健康，以远程监测健康参数。然而，可穿戴设备的小外形尺寸限制了电池尺寸和工作寿命。因此，这些设备需要频繁充电，这限制了它们的广泛采用。能量收集已成为可穿戴设备可持续运行的有效方法。不幸的是，仅能量收集不足以满足可穿戴设备的能量需求。本文研究了自适应能量管理的新问题，以实现可自我维持的可穿戴设备的目标，通过使用收集的能量来补充电池能量并减少用户的手动充电。为了解决这个问题，我们提出了一种称为 AdaEM 的原理算法。 AdaEM 背后有两个关键思想。首先，它使用机器学习（ML）方法来学习用户活动和能源使用模式的预测模型。这些模型使我们能够根据用户活动来估计一天中能量收集的潜力。其次，它对 ML 模型的预测和估计的不确定性进行推理，以使用动态鲁棒优化 (DyRO) 公式来优化能源管理决策。我们为DyRO提出了轻量级的解决方案，以满足部署的实际需求。我们使用用户活动的真实数据在由太阳能和运动能量收集组成的可穿戴设备原型上验证了 AdaEM 方法。实验表明，AdaEM 实现的解决方案与最优方案相差 5% 以内，执行时间和能源开销不到 0.005%。
Wearable devices that integrate multiple sensors, processors, and communication technologies have the potential to transform mobile health for remote monitoring of health parameters. However, the small form factor of the wearable devices limits the battery size and operating lifetime. As a result, the devices require frequent recharging, which has limited their widespread adoption. Energy harvesting has emerged as an effective method towards sustainable operation of wearable devices. Unfortunately, energy harvesting alone is not sufficient to fulfill the energy requirements of wearable devices. This paper studies the novel problem of adaptive energy management towards the goal of self-sustainable wearables by using harvested energy to supplement the battery energy and to reduce manual recharging by users. To solve this problem, we propose a principled algorithm referred as AdaEM. There are two key ideas behind AdaEM. First, it uses machine learning (ML) methods to learn predictive models of user activity and energy usage patterns. These models allow us to estimate the potential of energy harvesting in a day as a function of the user activities. Second, it reasons about the uncertainty in predictions and estimations from the ML models to optimize the energy management decisions using a dynamic robust optimization (DyRO) formulation. We propose a light-weight solution for DyRO to meet the practical needs of deployment. We validate the AdaEM approach on a wearable device prototype consisting of solar and motion energy harvesting using real-world data of user activities. Experiments show that AdaEM achieves solutions that are within 5% of the optimal with less than 0.005% execution time and energy overhead.
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影响因子：
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