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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Near-optimal energy allocation for self-powered wearable systems
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.
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- Award ID(s):
- 1651624
- NSF-PAR ID:
- 10062456
- Date Published:
- Journal Name:
- 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
- Page Range / eLocation ID:
- 368 to 375
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Thermophysiological comfort in humans is sought universally but seldom achieved due to biological and physiological variances. Most people in developed parts of the world rely on highly energy‐intensive, and inefficient central heating/cooling systems to achieve thermophysiological comfort which is rarely satisfactory. A potential solution to this issue is a wearable personal thermal comfort system (PTCS) consisting of textile‐based temperature and moisture sensors, thermal and moisture responsive actuators, and/or heating/cooling devices, that can sense the environment and physiology of the wearer, and accordingly provide an individualized thermal environment. Moving thermal regulation away from the built environment to the microclimate surrounding the human body using textiles has the potential to provide personalized thermal comfort and energy savings. Such a system may employ thermal comfort models and leverage the Internet of Things (IoT) and machine learning (ML) to understand individuals' comfort requirements. Herein, the current state of textile‐based active and passive comfort systems/technologies are summarized, including their environmental impact, major thermal comfort models, and factors influencing comfort. Also, active and passive textile‐based devices (sensors, actuators, and flexible heating/cooling devices) that may be incorporated into a textile‐based wearable PTCS are comprehensively discussed with an emphasis on their advantages, limitations, and prospects.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCAD.2017.8203801
