English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Advances in low-power electronics and machine learning techniques lead tomany novel wearable IoT devices. These devices have limited battery capacityand computational power. Thus, energy harvesting from ambient sources is apromising solution to power these low-energy wearable devices. They need tomanage the harvested energy optimally to achieve energy-neutral operation,which eliminates recharging requirements. Optimal energy management is achallenging task due to the dynamic nature of the harvested energy and thebattery energy constraints of the target device. To address this challenge, wepresent a reinforcement learning-based energy management framework, tinyMAN,for resource-constrained wearable IoT devices. The framework maximizes theutilization of the target device under dynamic energy harvesting patterns andbattery constraints. Moreover, tinyMAN does not rely on forecasts of theharvested energy which makes it a prediction-free approach. We deployed tinyMANon a wearable device prototype using TensorFlow Lite for Micro thanks to itssmall memory footprint of less than 100 KB. Our evaluations show that tinyMANachieves less than 2.36 ms and 27.75 $\mu$J while maintaining up to 45% higherutility compared to prior approaches.

tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices