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 μJ while maintaining up to 45% higher utility compared to prior approaches.
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GEM-RL: Generalized Energy Management of Wearable Devices using Reinforcement Learning
Energy harvesting (EH) and management (EM) have emerged as enablers of self-sustained wearable devices. Since EH alone is not sufficient for self-sustainability due to uncertainties of ambient sources and user activities, there is a critical need for a user-independent EM approach that does not rely on expected EH predictions. We present a generalized energy management framework (GEM-RL) using multi-objective reinforcement learning. GEM-RL learns the trade-off between utilization and the battery energy level of the target device under dynamic EH patterns and battery conditions. It also uses a lightweight approximate dynamic programming (ADP) technique that utilizes the trained MORL agent to optimize the utilization of the device over a longer period. Thorough experiments show that, on average, GEM-RL achieves Pareto front solutions within 5.4% of the offline Oracle for a given day. For a 7-day horizon, it achieves utility up to 4% within the offline Oracle and up to 50% higher utility compared to baseline EM approaches. The hardware implementation on a wearable device shows negligible execution time (1.98 ms) and energy consumption (23.17 μJ) overhead.
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- Award ID(s):
- 2114499
- PAR ID:
- 10423456
- Date Published:
- Journal Name:
- 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Wearable internet of things (IoT) devices are becoming popular due to their small form factor and low cost. Potential applications include human health and activity monitoring by embedding sensors such as accelerometer, gyroscope, and heart rate sensor. However, these devices have severely limited battery capacity, which requires frequent recharging. Harvesting ambient energy and optimal energy allocation can make wearable IoT devices practical by eliminating the charging requirement. This paper presents a near-optimal runtime energy management technique by considering the harvested energy. The proposed solution maximizes the performance of the wearable device under minimum energy constraints. We show that the results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/DATE56975.2023.10137228
